American Journal of Computational Linguistics 
COMPUTATION OF A SUBCLASS OF INFERENCES: 
PRESUPPOSITION AND ENTAILMENT 
p,RAVIND K. JOSHI AND RALPH WEISCHEDEL 
Department of C~~ and I~iar;l"on Science 
Moore School of Electrical Engineerihg 
University of Pennsylvania, Philadelphia 19104 
This work was partially supported by NSF Grant SOC 72-0546A01 
and MC 76-19466. 
Weischedel was associated with the University of California, 
Irvine, during the preparation of this manuscript. His present 
address is Department of Computer Scidnce, University of 
Delaware, Newark. 
Copyrlght el977 
Association for Computational Linguistics 
The term "inference1' has been used in many ways. In recent artificial 
intelligence literature dealing with computational linguistics, it has 
been used to ref= to any conjecture given a set of facts. The conjecture 
my be -true or false. In -this sense, "inferencet' includes mre than 
formally deduced statements. 
This paper considers a subclass of inferences, known as presupposition 
and entailment. We exhibit many of their pmperties. In particular, we 
demanstrate how to compute them by structural means (e.g. -tree transforma- 
tions). Fwther, we disc~s their computational properties and their mle 
in the semantics of natural language. 
A sentence S entails a sentence S1 if in every context in which S is 
true, St must also be true. 
A sentence S presupposes a sentence S" if 
both S itself entails St' and the (intermal) negation of S also entails S". 
The system we ha* described computes this subclass of inferences 
while parsing a sentence. It uses the augnated transition network (ATN). 
While parsing a sentence, the Am graph retrieves the tree tr,ansformations 
from the lexicon for any words in the sentence, and applies the tree 
 sfo or mat ion to the appropriate portion of the semaritic representation 
of the sentence, to obtain entailments and presuppositions. Ftrther, when 
a specific syntactic cons-truct having a presupposition is pamed, the Pfl3 
generates the corresponding presupposition using tree -formations. 
That presuppsition and entailment are inferences is obvious. 
However, the requirement in their definition that they be independent of 
the situation (all context not represented structurally) is stmng. Hence, 
it is clear that presupposition and entailment are s-trictly a subclass 
of inferences. As one would hope in studying a res-tricted class of a more 
geneml phenomenon, this subclass of inferences e~bits several computa- 
tional and linguistic aspects not exhibited by the geneml class of 
inferences. Some of these are 1) presupposition and entailment seem to be 
tied to the definitional (semantic) st~ucture ad syntactic structure of 
language, 2) presupposition and entailment e&ibit complex interaction 
of semantics and syntax; they exhibit necessary, but not sufficient, 
semantics of individual words and syntactic constructs, and 3) forthe 
case of presuppositibn and entailment,there is a na-1 solution to the 
problem of knowing when to stop drawing inference&, which is an importan-tr 
problem in inferencing, in genm. 
The term "inference" has been used in many ways. 
In recent artificial 
intelligence litemtire dealing with cqutational linguistics, it has been 
used to refer to any conjecture given a context (for instance, the context 
developed from previous text). 
The conjecture my be true or false. k 
this sense, "inference" includes mom than formally deduced statements. 
Further, alternatives to formal deduction procedures are so@t for 
computing inferences because formal deductive procedures tend to undergo 
ccmbinatori;ll expLosion . 
A subclass of inferences that we have studied are presupposition and 
entailment (defined in Section 1). As one would hope in studying a 
z-estricted class of a more general phenomenon, this subclass of inferences 
exhibits several computational and linguistic aspects not eXhibited by the 
gend class of inferences. 
One aspect is that presupposition and entailment seem to be tied to 
the definitional (semantic) structure and syntactic structure of language. 
As a consequence, we demonstrate how they may be computed by structural 
means (e. g. tree transfomtions) using &I augmented transition network. 
A second aspect is that presupposition and entailment exhibit complex 
interaction of semantics and syntax. They exhibit necessary, but not 
sufficient, semantics of individual words and of syntactic constructs. 
Another aspect relates to the problem of lawwing when to stop drawing 
inferences. 
There is a natwal solution to this problem far the case of 
presupposition and entailment . 
The definitions of presupposition and entailmnt appear in Section 1, 
with exmqles in Sections 2 and 3. 
A brief desoription of the system that 
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ccmputes the presuppositions land en-taiIme1a-t~ of an input sentence appears 
in Section 4. (The details of the camgutation and me system are in 
Weischedel (1976). Detailed comparison of this subclass of inferences 
with the genawl class of inferences is presented in Section 5. Conclusions 
are stated in Section 6. An appendix contains sample input--output sessions. 
In this section, we define the inferences we are interested in[ pm- 
supposition and entailment), and carment on our use of the tm "pwtics" 
2nd wcontext". 
In order to specify the sub-classes of inferences we are studying, we 
need same preliminary assumptions and definitions. 
Inferences, in general., 
must be made given a particular body of p~grratic informtion and with 
respect to texts. Sbce sentences are the simplest cases of texts, we are 
concentrating on them. Presuppositions and entailments are particularly 
useful inferences for studying texts havkg sentences containing anbedded 
sentences, and they may be studied to a limited extent independent of 
prap&ic jnformatian. 
1.1 Subformula-derived 
We assume that the primary goal of the syntactic cornpnent of a natural 
language system is to translate Awn natural language sentences to meaning 
representations selected in an artificial language. Assume further, that 
the meaning representations selected for Ihglish sentences have a syntax 
which may be appmximated by a context-free pm. By "approximated1', 
we mean that there is a context-free &ramm of the semantic representations, 
though the language given by the g~mrar may include sane strings which 
have no interpretation. (For instance, the syntax of ALGOL is often 
appmxhted by a Backus-Naur form specification. 
Since we have assumed a context-free syntax for the semantic 
representations, we may speak of the semantic representations as well-formed 
fcmulas and as having well-farmed subfmnulas and tree representations. 
As long as the assumption of context-free syntax for semantic 
representations is satisfied, the same algorithms and data structures of 
our system can be used regardless of choice of semantic primitives or type 
of semantic representation. 
Let S and Sf be sentences with meaning representations L and Lr 
respectively. If there is a well-formed subforuila P of L and sane tree 
trwmformation F such that 
Lf = F(P), 
then we say St may be subformula-derived h S. The type of -tree 
transfornations that are acceptable for F have been formalized and studied 
extensively in ccmnputat ional linguistics as f inite-state tree transformat ions. 
The main point of this work is that the presuppositions and entailments 
of a sentence may be subfomula-derived. We have built a system by which 
we my specify subformulas P and tree tmnsformations F. The system then 
automitically generates presuppositions and entaihmts from an input 
sentence S. 
1.2 Fmgmt ics and Context 
We use context to refer to the situation in which a sentence my 
occur. Thus, it would include all discourse prior to the sentence under 
consideration, beliefs of the interpreter, i. e. , in shwt the - state of the 
intqreter. We use pmtics to describe bll phenomena (and computations 
mdelling them) that reflect the effect of context. 
1.3 Ehtai3men-t 
A sentence S entails a sentence St if and onlv if in everv 
context which S is me, St is also true. We may say then that St is 
an entaibmt of S. This definition is used within linguistics 
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as a test rather than as a rule in a foml system. 
One 
discovers apirically whether St is an entailment of S by trying to 
construct a context in which S is true, but in which St is false. 
Entailment is not the same as material implication. 
For instance, 
let S by "John managed to kiss Mary,' which entails sentence Sf, "John 
kissed Mary. l1 Givon (1973) argues that even if N ST is true, we would 
not want to say that 'Wohn did not mage to kiss Mary. l1 The reason is 
that "managerf seems to presume an attempt. Hence, if John did not kiss 
Mary, we cannot conclude that John did not manage to kiss Mary, for he 
may not have attempted to kiss Mary. Though S entails S ' , it is not the 
case that S St, since that would require NS'SNS. 
We have shuwn that entailments may be subfda-der~ved, that is, that 
they may be computed by structural means. As an example, consider the 
sentence S below; one could represent its rrreaning representatbn as L. 
S entails Sf, with meaning representation Lf . 
S. John forced us to leave. 
L. (IN-m-PAST (force John 
(EVENT ( IN-THE-PAST (leave we ) ) ) 1 1 
Sf, We left. 
Lf. (IN=-PAST (leave we)) 
From the meaning representation selected it is easy to see the appropriate 
subfd and the identity tree transformtio~ which demonstrate that 
this is a subformula-derived entailment. (This is, of course, a trivial 
tree .h.ansformation. A nontrivial example appears in Section 1.4, for 
pnsupposition. ) Many ewmples of entailment axe given in Secticn 2. 
Notice that it is questionakde whether one understands sentence S or 
the word Ivforce" if he des not knaw that St is true whenever S is. 
In 
this sense, entailment is certainly necessary knowledge ( though not 
sufficient) for understan- natural* language. We will see this again 
for presupposition. 
A second, related concept is the not ion of presupposition. A - sentence 
S ~s~ticnlly) presupposes a sentence St if and only if S entails S' 
- - - -- -- - 
and the intmdl negation of S entails S ' . 
(Other definitions of presuppo- 
-- e_- 
- 
sition have been proposed, Kartumen ( 197 3 discusses various definitions . ) 
Fm the defhitlon one can easily see that all semantic presuppsZtions 
Sf of S are dtso entailments of S. Hawever, the converse is not true, as 
the sentence S and Sf above show. 
Again, this definition is primarily meant as a linguistic test for 
empiricdlly determining the presuppositions of a sentence and not as a rule 
in a formal system. 
Note that the hyth of a presupposition of a sentence is a necessary 
condition for the sentence to have a truth value at all. 
If any of the 
presuppositions are not true, the sentence is anomlous. 
For instance, 
the sentence 
'The -test prime number is '23. 
presuppoges that there is a greatest prime n-. The fact that there is 
none explaine why the sentence is anamlous. 
Other authors have referred to the concept of presupposition as 
*!given informationv. 
Haviland and Clark (1975) as well as Clark and 
Haviland (1976) suggest a process by which hms use given infc3rmation 
in understanding uttmces. 
They present much psychological and linguis- 
tfb evidence that confh their hypothesis. 
As an example of a subfonmila derived presupposition consider 
sentences S1 and S1' below. 
It is easy to see that whether S1 is true or 
false, S1' is assumed to be true. 
Sl: John stopped beating Mary. 
LJ: (IN-LTKE-PAST (stop (EVENT (beat John Mary) ) ) ) 
S1' : John had been beating Mary. 
11': (IN-THCPAST (HAVE-EN (BE-ING (beat John Mary)))) 
Ll and L1' are semantic representations for S1 and S1' respectively. 
The 
well- fomd subformula in this case is all af L3.. The tree transformation 
from W. to L1' offers a non~vial e-le of a subfonmila-derived 
presupposition. 
Notice that one might wonder whether sentence S1 and the meaning of 
"stopt1 were understood if one did not. huw that Sly rust be true whether 
John stopped or not. In this sense, presupposition is necessary (but not 
sufficient) knowledge for understanding natural language. 
We have shm that presuppositions (as we have defined them above) 
my be subfonmila-d-ved. Henceforth, we will use "entailment" to mean 
an entai3.nm-t whjch is not also a presupposition. 
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2. Elementary Examples 
Ms section is divided into two subsections, Section 2.1 deals with 
presuppositions, section 2.2 with entailments. All example sentences are 
ntmimred. 
An (a) sentence has as presuppsition or entailment the 
comsponding (b) sentence. 
2.1 Presupposition 
Presuppositions arise fm two different structural sources: syntactic 
constructs (the syntactic or relational strzlctur?e) and lexical items 
(semantic structure 1 . 
2.1.1 Syntactic constructs 
Perhaps the mst intriguing cases of presupposition are those that arise 
fhm syntactic constructs, for these demnstrate cwlex interaction 
between semantics and syntax. 
A construction bown as the cleft sentence gives 
rise to presuppositions for the corresponding surface sentences. Consider 
that if someone says (1) to you, you mi&t respond with (2a). 
1. I am sure one of the players won the game for us yesterday, but I do 
not knm who did. 
2. a. It is B who won the game. 
b. Scaneone won the game. 
The form of the cleft sentence is the word "it" followed by a tensed 
form of the word "be1', follawed by a noun phrase or p~positional phrase, 
followed by a relative clause. 
Note particularly that the presupposition (2b) did not arise frcw 
any of the individual words. Rather, the pempposition, which is clearly 
senwtic since it is part of the tmth qmditions of the sentence, arose 
fram the syntactic constmct. Thus, the syntactic (or relational) strum 
of the sentence can carry important samntic information. 
Cleft sentences illust~te one important use of presuppositions: m- 
reference. Cleft sentences asert the identity of one individual with 
anothw fidividual referred to peviously in the dialogue. 
Emher, the syntactic constructions associated with definite noun 
phmmes have presuppositions that their referents exist in the shared 
infoma-Lion between the dialogue participants. 
By "definite noun phrases1', 
we man noun phrases wkich make definite (as opposed to indefinite) 
reference . Such constructions include proper names, possessives, adj ect iVes , 
~~tpictive relative clauses, and nonres-bictive relative clauses. For 
example, consider the foll~g (a1 sentences and their wsociated pre- 
suppositions as (b) sentences. 
3. a. John's brother plays for the Phillies. 
b, John has a brother. 
4. a. The team that fhe Phillies play today has won three games in a mw. 
b. The Phillies play a team today. 
5. a. The Athletics, who won the World Series last year, play today. 
b. The Athletics won the World Series last year. 
''Restrictive relative clauses1' are relative clauses that m used to 
determine what the referent is. "Nonrestrictive relative clauses" are not 
used to determine reference, but rather add additional information as an 
aside to the main assertion of the sentence. (In written Wlish, they are 
usually beaded by camas, in spoken English by pauses and change of 
htonati~n.) 
Nata particularly that the dctive clauses as in (4) puppose 
mly that there is soone referent which must have that quality. On the 
other hand, nonrestrictive relative clauses, such as ( 5 ) psuppose that 
the particular object named also has in addition the quality mehtioned in 
the relative clause. Sentence (5a) might be taken as a parapkase of "The 
Athletics play today, and the Athletics won the World Series last year." 
Hmever, using the syntactic construct of the nonrestrictive relative 
clause adda the semantic infomatian that not only is (5b) asserted true, 
but also that (5b) must be presupposed true. Thus, this distinction between 
the restrictitie and nonrestrictive relative clauses demonstrates again that 
the syntactic construct selected can carry important semantic information. 
It is well-hown that one role of syntax is to expose (by reducing 
ambiguity) the relational structure of the meaning of the sentence. The 
examples of presuppositions of cleft sentences and restrictive and 
n~rwestrictive relative clauses demonstrate that another function of syntax 
is to convey part of the meaning itself. 
For other examples of syntactic constructs that have presuppositions, 
see Keenan (1971) andLakoff (1971). 
2.1.2 Lexical entry 
Presuppositions play an important part in the meaning of many words; 
these presuppositions my therefore be associated with lexical entries. 
Only a few classes of semantically-related words have been and-yzed so far; 
analyses of many words with respect to presupposition are reported in 
PiLlnwxe (19711, Givon (19731, and Kiparsky and Kiparslcy (1970). Examples 
and a surrmary of such ayses may be found in Keenan (1971) and 
Weisc3hedel (1975). 
All of the following eramples of presuppositions arise from the 
lexical entries for particular words. 
Again, the (b) sentence in each 
example is presupposed by the (a) sentence. 
The (very large) class of factive predicates prnvi.de clear examples 
of presuppositions, (see Kipamky and Kiparsky (1970) . Factive predicates 
may be loosely defined as verbs which take embedded sentences as subject or 
object, and the embedded sentences can usually be replaced by pamphr&ng 
them with ''the fact that S. '' 
6. a. I regret that The Phillies have made no trades. 
b. The Phillies have made no trades. 
Example (6) above demonstrates that another function of presuppositim 
in language is informing that the presupposition should be consided true. 
We can easily imagine (6a) being spoken at the beginning of a press 
conference to inform the news agency of the truth of (6b). 
It should be pointed out that presuppositions arising from lexical 
items have been studied primwily for verbs and verb-like elements such as 
adverbs. For instance, presuppositions have not, in general, beM associated 
with cmn nouns. 
Fillmore (1971) has found presupposition to be a very usefU concept 
in the semantics of a class of verbs that he labels the verbs of judging. 
For instance, (7a) presupposes (7b) and asserts (8b). 
On the other hand, 
( 8a) presupposes ( 8b) and asserts ( 8b) . 
Thus , "criticize1' and 
are in sane sense the dual of each other. 
7. a. 
The mnager criticized B for playing poorly. 
b. 
B is responsible for his playing poorly. 
8. a. The manager accused B of playing poorly. 
b. B' s playing poorly is bad. 
Keenan (1971) points out that some words, such as l~retwlnu, '!alsov, 
tttm~ , itagain", "other", and "anotherr1, carry the maning of sanething 
being repeated. These words have presuppositions that the item occurred 
at Seast one hfo* 
9. a. B did not play again today. 
b. B did not play at least once before. 
Note that these words include various syntactic categories. ttAlso" , "too" 3 
"again" , are adverbial elements (adjuncts ) . , and "anothert' have 
aspects of adjectives and of quantifiers. Again we see that the phenomenon 
of presupposition is a crucial part of the meaning of my diverse classes 
of words. 
Given these btmductory examples, let us turn our attention to 
examples of entailment. 
2.2 Entailment 
fitailments appear to have been studied less than presupposition. All 
of the examples identified as entailnmt thus far seem to be related to 
lexical entries of particular words. Tb canpehensive papers that 
analyze wrds having entailments are lkrtbmen (1970) and Givon (1973). 
2.2.1 Classification of words having entailmnts 
At least five distinct semantic classes of words having entailments have 
been identified by Karttunen (1970). In the following exmples, the (b) 
sentence is entailed by the (a) sentence. 
Redibates such as l%e be a positicplt, "have the oppmtmityu, and '%e 
&Left, are called "~nly-ifv verbs be~xiuse the embedded satence is entailed 
only if the predicate is in the negative. 
Far instance, (1Oa) entails 
(lob), but (11) has no entailment. 
10. a. The PhilHes were not in a position to win the pennant. 
b. The Phillies did not win the pennan-tr. 
11, a. The Phillies were in a position to win the pennant. 
Verbs such as ttforce", "causetr, and are "if" verbs, for the 
embedded sentence is entailed if they are in the positive. 
32. a. Johnny Bench forced the game to go into extra innings. 
b. 
The game went into extm innings. 
13. 
Johnny Bench did not force the game to go into extra innings. 
Note that (12a) entails (12b), but (13) has no such entailment. 
A "negative-if" verb entails the negative of the embedded sentence 
when the verb is positive. "Prevent1' and llres'train f& are such verbs. 
14. a. His superb catch prevented the runner fm scoring. 
b. The runner did not score. 
15. 
His superb catch did not prevent the runne fm scoring. 
mus, (14a) entails (l4b), but (15) has no such entailment. 
* 1 
The three classes of verbs above my be cabTed me-way implicative 
verbs; there are also two-way implicative verbs. Qlrch verbs have an 
entailment whether positive or negative. 
If the entailment is positive, we my call these "positive tm-way 
implicative" verbs. Exanples (16) and (17) illustrate "manage" as such a 
17. a. B did not me to win. 
b. B did not win. 
There are also "negative two-way implicative1t vefbs. Cansider (18) 
and (19). 
18. a. B failed to mke the catch. 
b. B did not make the catch. 
19. a. B did not fail to make the catch. 
b . B mde -the catch. 
For this clasa of verbs, the entailed proposition is positive if aad only 
if the implicative verb is negated. 
The five classes of words having entailments, then, are: - if, only if, 
negative if, positive two-way hplicat ive , and negative two-way implicative. 
All of the wmds cited in the literature as having entailments are 
predicates. In the examples here, my were verbs; SORE were adjectives 
such as "ableu. However, sm are nouns such as "proof If ; example ( 20) 
dammmtes this. 
20. a. The fact that he came is proof that he cams. 
b. He cares. 
We nuw turn our attention to various factors that must be accounted fw 
in ccaxputing pres~positions and entaihmts of canpound sentences. 
3. Catrplex IScamples: Mded Ehtailments and hsuppositions 
In this section, the fc3llcrwix-g question is considered: Suppose that a 
sentence S has a set of entaimts and a set of presuppositions. 
Suppose 
further, that S is &ded in another sentence St. Are the entahts 
and presuppositions of S also entailments and presuppositions of St as a 
whole? 
This has been referred to as the pmjection problem for entailmints 
and presuppositions. A solution to the pmblem invol~s miles for canbining 
semantic entities of mibedded (projected) sentences in ordm to compute the 
semantic entities of the whole sentence. 
A soxution to the pmjectian pmblem evolved in lbrtttmen (1973, 19741, 
Krttunen and Peters (19751, Joshi and Weischedel (19741, Smaby (1975) and 
Weischedel (1975 ) . The results are briefly mported here. A sumnary of the 
solutions my be found in Weischedel (1975). 
Kiwttunen (1973, 1974) divided all predicates into four classes: the 
speech acts, predicates of propositional attifude, connectives, and all 
o*er predicates. The classes were defined according to the effect of the 
predicate on presuppositions of Mded sentences. We found that the 
same classification was appru,priate far entailments, and extended the 
solution to inclMe entailments, as well as presuppositions. 
3.1 Presupposition 
As an example sentence, consider (11, which presupposes (2). 
1. 
Jack regretted that John left. 
2. John left. 
In tha folluwing sections, we will consider the effect on presupposition (2) 
of embeddk.lg (1) mdes varh predicates taking enbedded sentences. 
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3.1.1 Moles 
- - 
Many predicates taking embedded sentences could be called holes 
because they let presuppositions of embedded sentences throm to betrone 
presuppositions of the compound sentence. "harett is such a predicate; 
( 3) presupposes (2 1 , 
3. Mary is aware that Jack regretted that John left. 
All prwdmtes taking embedded sentences, except for the v@bs of saying, 
the predicates of ppositional attitude, and the connectives appear to be 
holes. 
3.1.2 8peech acts 
The vebs of saying, or "speech actrf v&s, permit the presuppositions 
to rise to be presuppositions of the conqpoUhd sentence, but those presupposi- 
tions are embedded in the world of the claims of the actm perfoming the 
speech act. Smdby (19-75) first pcinted out this impofiant fact. 
For instance, (4) presupposes (5), not (2). 
4. Mary asked whether Jack regretted that John left. 
5. Mary claimed John left. 
3.1.3 Predicates of propositional attitude 
Analysis of predicates af propositional attitude is very similar to 
that of speech acts. SOE predicates of popositicnal attitude are "believe", 
It-" 
, and ''hope". In general, presuppositions of sentences embedded under 
such a predicate must be embedded under the predicate t'beLievell to reflect 
that they are preaippositions in the world of the actcxr's beliefs. This was 
first pointed odt by hrttmen (1974). 
FOP example, (6) (71, not (2). 
-20.1 
6. Mary thinks Jack regxemed that Joh left. 
7. Mary believes John left. 
3.1.4 Connectives 
The effect of connectives is rather complex, as (8) and (9) demrmsmte. 
Sentence (8) presupposes (21, but (9) clearly does not. 
8. 
If Jack was there, then Jack regretted that John left. 
9. If John left, t%en Jack regretted that John left. 
Let A and B be the antecedent and consequent respectively of the conpow1d 
sentence "if A -then B". 
The examples of (8) and (9) are complex, f &r they seem to demonstrate 
that the context set up by the antecedent A must be part of the canputation. 
This would in general requh complex theorem provers in order to determine 
whether the presuppositions of B are implied by A, and therefm are not 
presuppositions of the canpound sentence. Huwever , Peters suggested (a 
footnote in Karttunen (1974)) that the presuppositions of "if A then B," 
(where rraterial implication is the interpretation of "if - then") , arising 
from the presuppositions of B are of the form "if A then C", where C is a 
presupposition of B. 
Further, all presuppusitions of A are presupposit~ons 
of "if A -then B. " This suggestion eliminates the need for theorem proving 
and offers instead a simple computation similar to that for the verbs of 
saying and the verbs of prop0siticma.l attitude. 
FW the examples given then, (8 > psupposes (10) , and (9 ) presupposes 
(11) which is a tautology. 
10. If Jack was there, then John left. 
U. 
If Jdhn left, then Jdhn left. 
Qla may easily wiky that (8) preqpxes (10) by a tnrth table mwutatim. 
mmen (1973 ) argues that the solution of "A and $3" reduces to the 
solution of "if A then BIT and that the solution to "A or Bw reduces to the 
solution of "if not(A1 then BVi . 
This completes the description of the four classes of embedding pmdicates 
and the& effect on embedded ~suppositions. However, there is another 
phenanenon , that of enibedded entailments beaming pesuppositions of 
canpound sentences. 
3.1.5 Ehta.$hents pmted to presuppositions 
Clearly, any entailment of a presupposition must be a presupposition 
also; Ms is evident frwn the definitions. For instance, (12) presupposes 
(13). Since (13) entails (141, (14) must also be a pmpposition of (12). 
12. Jack regretted that John1 s children forced Mary to leave. 
13. John's children forced Mary to leave. 
14. Mary lee. 
The five cases disussed above outline a solution to the projection 
pmblem fop presuppositions. 
3.2 Entailments 
In the examples, we will embed (15) under various predicates, to see 
hm the c~taihnt (16) of (15) is affected. 
15. Fred prevented Mary fmPn leaving. 
16. Mary did not leave. 
3.2.1 Chain of entailments 
Corrresponding to the class of holes for presuppositions, two cases 
arise for entaihmts. One case was cove in 3.1.5; entaihents of an 
embedded sentence which is a presupposith are ~ppositims of the 
A second, disjoint case involves setting up a chain of entai-ts. 
For instance, (17) entails (15) whidh entails (16). 
17. John forced Fred to prevent Mary frcpn leaving. 
This is truly a chain of entailments, since breddng a link in the chain 
causes embedded entailments to be blocked. 
For instance, the presence 
(absence) of negation is crucial; if (17) were negative, it would not entail 
(15) nor (161, though (171 did. 
Thus, for the case ~nvolving a chain of entailments, the entailments of an 
anbedded sentence are entailed by the compound sentence only if such a 
chain of entailments can be set up. 
3.2.2 Speech Acts 
Smaby has pointed out that there are at least two subclasses of 
speech act verbs according to behavior of abdded entailments. Further, 
the syntactic shape of the embedded sentence affects entailments. 
For instance, if the syntactic shape of an embedded sentence S is 
Whether S or nottt, "for NP to W1', or "if S" , all enibedded entailments are 
blocked. For instance, (18 ) entails nothing about Mary's leaving. 
18. 
John asked whether or not Bed prevented Mary from leaving. 
However, a "wh-sca-ne" embedded sentence (beginning with "who", "what", 
ffwhen", frwhich", etc . ) have all entailments of the embedded sentence promoted 
to presuppositions, since the erribedded sentence is presupposed. For 
instance, (19 presupposes (201, and therefore presupposes that Vary did 
not leave". 
19. John asked who prevented Mmy from leaving. 
20. Scmeone p'reventd Nary fbn leaving. 
For enbedded sentences of the fom "that St!, we notice -two subclasses 
of speech acts. Verbs such as "sayn, 'tdeclare", and ltaffh'v are like 
Itif predicateslT, fop embedded entailments are not blocked if the verb is nat 
in the negative. Hcrwever, in the positive, the enibedded entaLhen't:s became 
entailments of the ccnpund sentence, but under the speakerrs claims. For 
instance, (21) entails (22). 
21. John said that bed prevented Mary fm leaving. 
22. Job clximd that Mary aid not leave. 
A second subclass of verbs includes "deny". They are analogous to 
"negative if verbs1'. When "denyu is in the negative, embedded entailments 
are blocked. Hawever, when lldenyN is positive the entailnmts of the 
negative form of the anbedded sentence are entailed by the c-md 
sentence, but under the speakert s claims. For instance, (24) is entailed 
by (23). 
23. John denied that Mary was able to leave- 
24. John claimed that Mary did not leave. 
3.2.3 Prediqates of propositional attitude 
Srraby (1975) analyses these predicates in the same way as the speech 
acts. "Believet' , "think" , and "suspect" are examples of a subclass 
analogous to ''if predicatesv1 or to "say", "declare1', and %ffhvV. 
v~ubttt 
is an example of a second subclass analogous to "negative two-way Implicative 
predicates" such as "failv. 
Thou@ the subclasses for predicates of propositional attitude are 
analogous to those of *e speech acts, the Mded ent8i-ts of 
pmpositiondl attitude mcdtes bedane entaihmts of the caqmund sentence 
underthe 
U~S, ratherthan\mdeerthe speaker's daims as in the 
speech act case. For instance, ( 2 5 ) entails ( 26 ) , 
25. John thought that Fred prevented Mary frwn lea*. 
26. John believed that Mary did not leqve. 
3.2.4 Connectives 
For "if A then Bn , the entailments are of the form "if A then Cfr , 
where C is an entailment of B, For ''A and B" , the entailments are the 
union of the entailments of A and of the entailments of B, since both A 
and B are entailed by "A and B". For "A or B", thm do not seem to be any 
useful entailments. 
This concludes the analysis of .the projecticpl problem far 
presuppositions and ent-ts. 
4. Outline of the solutions in the system 
The purpose of this section is to give an overall view of the system 
and an outline of the methods used to compute presupposition and entailment. 
For a mre complete, detailed description of the computational methods and 
the system see Weischedel (1976). 
Section 4.1 presents a block diagmm 
of the system; 4.2 briefly outlines the cconputation for ae various 
examples of sections 2 and 3; section 4.3 attempts to state some of the 
limitations of the system, including the mry and the requkmn^ts. 
4.1 Block diqpm 
A block diagram of the system appears in Fiv 4.1. All arrows 
represent data flaw. A sentence S in English is input to the system. The 
parser is mitten as an augmented tMnsition network graph (Am). 
(Woods 
(1970 specifies the A!I'N as a formal We1 and as a programnhg language. ) 
hbile parsing, the PlTN refers to the lexicon for specific infopmation for 
each wrd of the sentence S. Laical information is of three types: 
syntactic informtion, informtion for generating the serrtlntic representation 
or translrtion , and information for making lexical inferences --presuppositions 
and entailments. The organization of the lexicon for computing lexical 
inferences (psuppsitions and entailments) is a novel aspect of the system. 
Fmm the definition of presuppositims and entats, it is clear 
that the system heeds a set of functions for mnipdating or trhnsfcaming 
trees. These appear as a sepamte block in Figure 4.1. The parser 'Us 
them while parsing; this is represented in the diagram as input I and value8 
1' of fimctions. These funeths are written in LISP. 
Figure 4.1 
System Structure 
(All arrows represent data flow) 
Am 
Graph 
(Parser) 
-27- 
Using the lexical information, the relatiad or syntactic strzcture 
of the sentence, and the tree -bansfomtion functions, the parser generates 
the semantic representatio~ (translation) t of -the sentence and a set of 
presuppositions P and entailments E of the sentence. 
Since each presupposition P and entailment E is in the logical notation 
of the semantic representations of sentences, a small tmnsformational 
cutput component has been included to give the presuppositions and entailments 
as output in English. These appear as P' and E' ; in Figure 4.1. The trans- 
formatima output; cqnponent iS also wri Ren in LISP. This output component 
is very small in scope and is not a major component of the work repofled 
hem. 
4.2 Outline of solution 
Al sketch of the computation of presupposition and entaibmt is 
presented here ; details of computation are presented in Weischedel (1976 ) . 
There are four fundamental phenomena exhibited in secticms 2 and 3 : 
presuppositions; fhm syntactic mnstructs, presuppositions fram particular 
words (lexical entries), entailments from lexical entries, and the projection 
phencunena. 
In der to compute presuppositions frwn syntactic constructs, two 
principles are inportant; detecting the syntactic construction and dealing 
with anibiguity. 
Syntactic constructs are syntactiddlly mked in the 
sentence. Thus, the pars& may be constructed such that thwe is a parse 
generated when those syntactic markings e present. Tn the ATN, one may 
mnsmct the graphs representing the gmmmr such that there is a particular 
path which is traversed if and only if the syntactic ccnstruct is present. 
Then, we may lassociate with that parti- path the txee .trensforsmtion 
yielding the presupposition of that syntactic construct. 
For instance, 
cleft sentences are syntactically marked as the ward I'itU, followed by a 
tensed form of l1beH 9 follwed by either a noun phnase or a prwpositianal 
phnase, follcrwed by a relative clause. 
The path(s ) in the g~ph might be 
as below* 
Associated with this path would be a trivial -tree transformation which 
re-tums the semantic representation of the relative clause as a presupposition, 
The second principle deals with ambiguity. Even though we have 
structured the gmphs in the way above, the same surface form may arise 
from two different syntactic constructs, one having a presupposition and 
the other not. In such a case, our systm (and in fact any parser should 
be able to give semantic representations for both parses; with one paz-se our 
system yields a presupposition, with the ot- parse our system would not 
have the presupposition. It is the role of gens semantic and -tic 
ccmponents to distinguish which semantic representation is intended in the 
context. In fact, the difference in the presuppositions with the differing 
parses is one criterion which general semantic and --tic canponents could 
use to resolve the anibiguity. 
Fc& generating presuppositians of words (lexical entries) , the chief - 
pmblems are how to encode the tree tmnsformatiar in the lexicm (dictio~xy) 
-29- 
and when to apply it duping parsing. 
In general a tlP6 tmnsfmtioll waiLd 
have a left hand side which is the pattern to be mtched if the 
transformation is to apply and a right hand side giving the tMnsformed 
swcture. 
The mason we can encode the left hand side in ?As gmmmr is simple. 
All of the examples in the litemture deaiing with presuppositions fmn 
lexical entries have in comn the fact that the existence of the p~- 
supposition depends only upon the syntactic envir0m-t of the word and the 
word itself. Hence, we can structure the g~ph of the grammar in a way 
that the paths correspond to the necessary syntactic envirorunents. Upon 
encountering a word of the appmpriate syntactic category in such a 
syntactic enviromt, the system lmks in the lexicon under that word for 
the (possibly empty) set of right hand sides of tree tmnsfomtions. 
The way of writing the right hand sides assurnes that the parser k 
t~versing a path undoes the syntactic construct encoded in that path, and 
assigns the components of the smmtic representation according to their 
logical role in the sentence rather than their syntactic mle. (This is not 
a new idea, but rather has been used in several systems pre-dating ours, As 
an example, the semantic representation of llMaryl' in the following three 
sentences muld be assigned to the sam register while parsing, "John gave 
Mary a ball", "Mary was given a ball by Johnn, and '?A ball was given to Mary 
by John". Thus, we can assume a convention for nanf5-g registers and 
assigning components of the sexlantic representation to them, independent of 
the syntactic enviromt. To encode the right hand side of the tree 
transformation, we we a list whose first elmt is the .tree structure ~5th 
constants as literal am and piticns of variables as plus cigns. The 
-30- 
rerraini?lg elements of the list specie the registers to fill in the variable 
psi-tions . 
This, then, is how we intepte the tree trensformations for 
presuppositions into the parse. The lexical examples fm entaLh=nt also 
must employ tree tmnsfomtions but are complicated by the five different 
classes of predicates yielding entailments and their dependence on whether 
me sentence is negated or nat. A further canplicatim was illust~ted 
in section 3, for a chain of entailmnts must be set up. 
For mtailments, we encode the left hand side and right hand side in 
the same way as the lexical examples of presuppositions. However, for 
entailments, for each ri&t hand side we also encode -three other pieces of 
information. They are the pre-condition of whether negation must be present 
(or absent), whether the entailed pmposition is negative or not, and whether 
the entailed propositional. corresponds to the left sub-tree crr right sub-tree. 
At each sentential level, we verify that the left hand side of the me 
trwsfomtion is present. If it is, we make the transformation indicated 
in the lexicon and save the resulting proposition along with the otheE three 
pieces of informtion mtioned above associated with it. We save this in 
a binary tree, one level of tree sentential level. It is a binary tree 
since all predicates taking embedded sentences seem to permit only one or 
two of its arguments to be anbedded sentences. 
Upon hitting the period (or question mark), all of the negation 
information is pmsent so that we my simply traverse the tree fraan the mot, 
doing a compx?ison at each level to verifv that the mditions for negation 
being present (absent) are met. This caapletes an outline or amputation 
of at-ts. 
Next we outline a solution to the projection problem, The struc~ 
mlution to the projection plpblem wed in section 3 has simple 
computatiorpl requbamts. We have stxucttired the gmphs such that 
mcucsion OCCUPS for each embedded sentence. At each sententh1 lewl, the 
pph returns as a value a list of at least four elements: the semvltic 
representation of the sentence at this level, a list of presuppositions of 
this sentence and any embedded in it, a tree as described above for computing 
entailment at this and lower levels, as well as a list of semantic 
representations of noun phrases encountered at this UP lawer levels. 
Just before popping to a higher sententid level a projection function 
is applied, which is merely a CASE statement for the four? cases described in 
sectLon 3. For holes, nothing is changed. For speech act predicates and 
of propositioMll attitude, the pxsuppositims of enbedded sentences and 
ppositions in the tree for entailments are enbedded under a special 
semantic primitive C CLAIM for speech acts, BEIlEVF, for verbs of propositional 
attitude). 
Ehibedding under these primitives places the presuppositions and 
entaibents in the world of the actor's claims or beliefs. 
For connectives, the ccmputation is just as described in section 3. 
Again, an embedding is involved, this time under a semantic primitive IF-DEN 
to place the propositions in the mrld of the context created by the left 
sentence of -l%e connective. 
We have only outlined hew to qute presupposition and entaihnt. 
Many interesting and complex aspects of the cartputation are detai3ed in 
-32- 
Weiachedel. (1976 1. For instance, external negation affects the c~mputation 
of presupposition, as does syntactic envbmmmt. In general, the tense and 
time of presuppositions or entailments oannot be cmputed simply by filling 
slots in the semantic representation of the inference with registws con- 
taining pieces of semantic representation of the input eentence; Them, 
a ~~zation of the BUILD function of an ATN is needed. Mer, the 
cmputationd. means to accum't: for the effect of negation on entailments of 
errbedded sentences, for aibedded entaihents mted tm presuppositions, 
and for the effect of opague and tmnsparent mference on presupposition are 
pmsented in Weischedel ( 1976 ) . 
4.3 what the SystemDOes Not Do 
The limitations of the system are of two kinds: those that could 
be handled witbin the fhm wok of the system but are not because of limits- 
tims of man-hours, and those that could not be handled wi* the present 
flxamwrk. 
4.3.1 Limitations that could be removed 
The system is currently limited in four ways, each of which could be 
removed, given time. One set of restrictions results froen the fact that 
our pragrmn represents only a small part of a camplete natural langauge 
processing system. Only the syntactic component is included (though these 
inferences, which are semantic, are canputed while parsing). fQ a 
consequence, no anbiguiq is resolved except that which is syntactically 
resolvable. 
Second, though a trmsforsm.tional output component is included to 
facilitate reading the output, it has a very limited range of constructions. 
The principles used in designing the component are sound though. 
A third aspect is caputation time. Since our main interest was a new 
type of computation for a syn-tactic ccxnponent, we have not stressed 
efficiency in time nor storage; rather, we have concentrated on writing the 
system fairly rapidly. Considering the nLnnber of conceptually simple, 
efficiency meesures that we sacrificed for speed in implementing the system, 
we are quite pleased that the average CPU time to canpute the presupposition 
and entaihnts of a sentence is twenty seconds on the DEC -10. The 
nmerory requirements were 90K words including the LISP interpreter and 
interpreter for aqpnts transition networks. For further details and the 
simple economies that we have not used, see Weischedel (1975). 
As a fourth class, we mention the syntactic constructions allowable as 
input to the system. We have not allwed several complex syntactic problem 
a& are essentially independent of the pru>bf,ems of ccmputing ~ppositions 
and r ntailmwts, such as oonjunath reduction, c2cap1ex anapharic reference, 
ar prepositional phrases on ram phrases. (A resursive transiton network 
is given ir Weischedel (19761, indicating exactly what syntactic constructions 
are implemented. 
The n* of English quantifiers in the system is smaJ.1. Also the 
dietionmy is of very modest size (approximately 120 stem wrds ) . However, 
our ledcon is.pattmed after the lexicon of the linguistic string parser,. 
which includes 10,000 wcods. Therefore, we have avoided the pitfall of 
gmmatical ad hocness. (The Linguistic s~ing parser is described in 
Eiage~ (1973). 
We have not included nodal tenses or svbjunctive mod. This is be- 
cause the effect of mDdals and the subjwtive 0 on presupposition and 
entailment has mt been fully mrked out yet. A limited solution for mcrdals 
and subjunctives has been wozked out fa a micro-world of tictaotoe in 
Joshi and Weischedel (19 75 ) . 
4.3.2 Tihitations difficult to remove 
We have dealt with specific time elements fw presupposition and 
entailment in a very limited way. Time has been explicitly dealt with only 
for -the aspectual verbs; however, time is implicitly handled in detail for 
all presuppositions and entaihmts thmugh tqe (see Weischedel 1976)). We 
have not included time otherwise, because we feel that .the same solution 
presented far assigning tenses to pesuppositim and entat my be 
adapted for explicit the elemmts. 
A rime serious difficulty would arise if pre,suppositicns or 
entailmjmts were discowred whiceh depend on different information than any 
considered up until this time. For instance, the occurrence of presuppositions 
thus far di$mvered has depended only on syntactic constructions, lexical 
entries, and fhe four classes of enbedding predicates (holes, comectives , 
speedh acts, and verbs of pr'spositional attitute). The existence of 
en-tailmenfs t?ius r'ar encountered has depended only on negation, syntactic 
constructions, lexical entries, and the four classes of -ding predicates. 
It is conceivable that presuppositions and entailments will be 
discovered which depend on other entities; for instance, presuppositions ar 
en lxibents of sorne predicate might be fourid to depend on the tense of the 
predicate. If such hanples are found, different means qf wit* lexical 
eneies muld have be devised in order i~ encode these depehdencies. 
5. Rale of presupposition and entaihmt 
In section 5.1, the role of preeuppcsitbn and entailments as 
inferences is pinpointed. In section 5.2, the use of emtic pr-tiws 
is considered. 
5.1 Xnferring 
The tm ninfemncelf has been used recently to refer to any 
conjecture made, given a text in scme natwal language. Chamiak (1973, 
19721, Schank (19731, Schank and Rieger (19731, Schank, et. dl. (19751, and 
Wilks (1975) mncenmte on such inferences. All of We projects seek scme 
caqutational means as an alternative to farmdl deductive pmcedures 
because those tend to cambinatorial explosion. 
That presupposition and entailment are inferences is obvious. Howv~r; 
the re~~t in their definition that they be independent of the 6ituath 
(all context not =presented strucUly) is strong. For instance, fkm 
sentence S Wow, one might feel that St should be entailed; yet, it is not. 
S: John saw Jim in the hall, and Mary saw Jim in his office. 
S : John and Mary sw Jh in different places. 
By appropriately chosen previous texts, St need not be true whenever S is. 
For example, the previous text might indicate that Jimt s office is in the 
hall. general, ccwnon nouns do not seem t~ offer many examples of 
presupposition and entailment. bxn the example, it is clear that 
presupposition and entailment are strictly a subclass of inferences. 
Presupposition and entailment are a subclass of inferences distinguished 
in several ways: 
First, presuplpsiticm and entailment are reliable infmces, 
mmep than being dy amjectures. ~poeiticols are true whether the 
sentence is true 011 fabe. Entaihmts AUgt be true f.f the sentence is true. 
Se@, presupwsition and entailment are Wmces that seem to 
be tied to the structure of language, for they mise frcm syntactic structure 
and fkun definitiad strucm of individual words. The fact that they 
are tied to the s~~ of language enables them to be caputed by 
s'h?utxuml means We. , tree tmmsfcrrmations 1, a canputational means not 
appropriate far all inferences. 
Furthmre, since presupposition and entailment are tied to the 
syntactic and definitional structwe of language, these inferences need to be 
made. For instance, upon encountering "John was mt able to leaveft, one 
really 3ws want to infer the entailnvnt that "John did not leave". Whether 
or not it is wise t.., ccmpute conjectural inferences, on the other hand, does 
not have a bL~ip1e answer, by virtue of their conjectural nature. 
A fourth distinction of presupposition and entailment is in the problem 
of knowing when to stop inferring. Inferences thanselves can be used to make 
other inferences, which can be used to make still mre inferences, etc. When 
to stop the inferences is an open question. Presupposition and entailment, 
as a subclass of inferences, do not exhibit such a chain xeaction of 
inferences. The reason is that pnesupposition and entailment arise fhm either 
the individual words or the particular syntactic constructs of the sentence; 
psuppositions and entailments do not themselves give rise to ms~e infemces. 
We my smmr5-~e these distinguishing aspects of presupposition 
and entailment by the fact that presupposition and entailment are inportant 
semantically for understanding words and syntactic constructs. This does not 
deny the importance of other, inferences; conjectuml inferences are necessary 
torepresant pragarrtic aspect8 of nanahrral language. 
-38- 
The role of presuppositim and entaihent in a mete naw hqpqp 
pmwshg system, then, is that they are a subcbass of the inferences which 
the system must ccnnpute. Infmce8 in gd are made fmn an input 
sentence in mjunction with the systemv s model of the context of the situation. 
Presupposition and entailment are a subclas of inferences associated with 
the semantic strucW of particular wrds and with the syntactic structure 
of the sentence. Thus, as we have shown, they my be cmputed while parsing 
using lexical information and grwmatical hfoxl~tion. The systemrs model 
of the context of the situation is not needed to compute the psuppositi~ns 
and entailmen-ts for any reading or interpretation of a sentence; of course, 
to ascertain which reading or interpretation of a sentence is intended in a 
given context, the system's rmdel of the context is essential. 
5.2 Sefiantic primitives 
Semantic primitives have been investigated as the el-t with which 
to associate inferences. (See Schank (19731, Schank, et.al. (1975), 
Yammashi (1972 )) . This has the important advantage of capturing shard 
infmces of many similar words by a semantic primitive, rather than repeating 
the sawtic hfm-tion for -those shared inferences for each word. Inferences 
would be made in the semantic aqonent. 
The assumptions of our canputation do not preclude the use of primitives 
in smantic representations. On the oontrary, the particular $emantic 
mpresentations our system uses do include primitives. Hmvm, we have not 
associated the canputation of mpposition and entz%Lmmt *th semantic 
primitives 
The reason is that presuppositiavls arise fnm syntactic -cohstructs, 
as well as fran the semantics of particular wads. Wher, syntactic 
s.tryctwe aan inteMct with the entailments of words, as in the follaJing 
~le . Because St is presupposed by S, SVt becrmes a presuppositim of S, 
nat merely an entailment. 
S Who prevented John fhn leaving? 
S' Someone *vented John fkm leaving. 
St' John did not leave. 
To mute such effects in the semantic ccmponent, sufficient syntactic 
s~~ of the surface sentence would have to be available to the semantic 
cmponent. Whether that is possible or whether that would be wise is not 
clear. 
For that reason, we have not used semantic primitives to compute 
 position and entailment. 
6. Conclusion 
'Tk mein goal of this work is its de~~>nstmticm of a met- fca. w+ting 
the lexicon and parser for the computaticm of presupposition and entai-t, 
and its exhibition of the procedures and data structures necessary to do this. 
Presupposition and entailment camprise a special class of inferences, 
distinguished in me ways. 
First, they both may be canputed strmc-y 
(by 'bree ~sfomations) , indepaWt of context not inherent in the 
stmctum. 
Second, altho* inferences in general are conjecturgl, 
presupposition and entailment may be reliably asserted; entailments are true 
if the sentence entailing them is true; presuppositions are true whether 
the sentence presupposing them is true or fdLse. Ihird, since presupposition 
and entailment are tied to the definitiondl and syntactic structure o? the 
language, they do not spam themselves nor lead to a chain reactLon explosion, 
as other Smces may. 
We suggest two areas of future research. One is to derive a means of 
accounting for presuppositions arising from syntactic constructs, in a way 
consistent with using semantic p~imitives to account for lexical examples 
of presupposition and entailment. 
A second area is suggested by the interaction of syntax and semantics 
evident in presuppositions arising from syntactic constrw-ts. A study of 
phenomena that cut across the boundaries of syntax, semantics, and pragnatics 
and a ccmputational mdel incorporating them could prove very fruitful to our 
understanding of naW languages. 
Indluded here is the output for seveml exemplary sentences. 'The 
semantic representations m a function and argwmt notaticpl developed by 
Harris (1970) snd Mied by Keenan (1972). As in logic, variables are 
bound otitsi.de of the foxmula in which they are used, Any semantic primitives 
my hi% beusad, a hl,g aS they €?JIlP100y the functb - ZWgWeIlt SyIlta~. Btd.3.8 
about the semantic mpresenticxm my be found in Weischedel (1975). 
We new describe the format of the output. 
The first item is the 
sentence typed in. Note that /, mans carma and / . means period, -we of 
LISP delilniters. 
The searantic rep~sentation of the inplt sentence itself is printed 
next, under the heading ff-C RFPRESENPATIOWt. 
Presuppositions not related to the existence of referents of noun 
phrases are printed under the 1-1 r'NON-NP REWPFQSITIONSfl. Presuppositions 
about existence of referents of noun phrases are winted .under the label 
ltNP-mTED F%ESUPPOSITIONSF1. The set of entailments follows the 'labeJ, 
~1ENTA7:mS". If for any of these sets, the set is empty, then only the 
Label is printed. For the two sets of presuppositions and the set of 
entailments, the semantic pepresentation of the set of entailments in 
Keenan's notation is printed first, then the English -phrase genemted 
by the output component. 
Tn some cases the tense of a presupposition is not loxxun. In sufh 
instances, the output component prints the stem verb followed by the ~p1b1 
r',mm". 
Examples of presuppositions frcm syntactic constructs appear in 
examples 1 and 2 ; the clef't construction gives a presupposition in 1; the 
definite noun phrase in 2 gives a presupposition. Presuppusith frrrm 
l&& entries appear in 3 and 4. 
lTOnly" in 3 has a preqpmition; "fril" 
in 4 also has a presupposition. 
Capa~ing 4 and 5 demns-tM.te8 the 
canputation of a chain of entailments. 
Se- examples of the projecticn problem have been included. Ekaples 
of predicates which are holes appear in 4 and 5. The effect of speech acts 
appears in 6. The effect of "if . . . then1' (interpreted as mterial. implication) 
is evident in 7 and 8. 
The terminal sessions follow. 
IT IS DR SMITH WHO TEACHES CIS591 /, 
SEMANTIC REPRESENTATION 
( (CIS591 /, X0006) ((DRSSIYITH /, X0B05) (ASSERT T (IN-THE-PRESENT (BE 
IT (IN-THE-PRESENT ('MACkl X00B5 NIL X0006)) ) ) ) ) ) 
NON-NP PRESUPPOSITIONS 
rss91 /, xaae6) ((E INDIVIDUAL /, ~0005) (IN-THE-PRESENT (TEACH x0 
NIL X0006)))) 
SOME INDIVf DUAL TEACHES CIS591 
NP-RELATED PRESUPPOSITIONS 
((DRSSMITH /, X0085) (*UNTENSED (IN-THE-SHARED-INFO X00i35))) 
DR SMITH EXIST -UNTENSED- IN THE SHARED INFORMATION 
( (CIS591 /, X0006) ("UNTENSED (IN-THE-SHARED-INFO X00R6) ) ) 
CIS591 EXIST -UNTENSEDa IN THE SHARED INFORMATIOH . 
((DRSSMITH /, X0005) (*ONTENSED (HUMAN X0005))) 
DR SMITH BE -UNTENSED- HUMAN 
ENTAILMENTS 
Example 1 
-45- 
THE PROFESSOR THAT I ADMIRE BEGAN TO ASSIGN THE PROJECTS /. 
SEMANTIC REPRESENTATION 
((((COLLECTIVE PROJECT /, X0010) (NUMSER X0810 TWObOR-MORE)) /, X8017 
) (( ( (THE PROFESSOR /, XdBcb8) (IN-THE-PRESENT (ADI4IRE I XU805)) ) /, X 
00@9) (ASSERT I (IN-THE-PAST (START (EVENT (ASSIGN X0B09 NIL X0817)) 
NIL))))) 
NON-NP PRESUPPOSITSONS 
( ( ( (COLLECTIVE PROJECT /, XB010) (NUMBER X0018 TWO-OR-MORE) ) /, X3817 
) (( ((THE PROFESSOR /, XOBLIB) (IN-THE-PRESENT (ADMIRE I- X@e)W))) /, X 
0009) ((((E TIME /, XOa18) (IMMEDIATELY-B$FOR2 X0018 NIL)) /, X0019) 
(AT-TIME (NOT (IN-THF-PAST (HAVE-EN (BE-ING (ASSIGN X0009 NIL X0017)) 
) ) xe@3RS 1 1 1 
IT IS NOT THE CASE THAT THE PROFESSOR THAT I ADMXRE HAD BEEN 
ASSIGNING THE PROJECTS 
NP-RELATED PRESUPPOSITIONS 
([((E PROFESSOR /, X0008) (IN-THE-PRESENT (ADMIRE I X0008))) /, X0009 
) = (*ONTENSED ( IN-THE-SHARECf-INF3 XB'dd9) ) ) 
SOME PROFESSOR THAT I ADMI RE EXIST -UNTENSED- IN THE SHAREO 
I NFORMATION 
(( ((E PROJECT /, X0010) (NUMBER X0010 TWO-OR-MORE)) /, X0017) ("UNTEN 
SED (IN-THE-SHARED-INFO X0017))) 
SOME PROJECTS EXIST -UNTENSED- IN THE SHAREO INFORMATION 
ENTAl LMENTS 
((((COLLECTIVE PROJECT /, XO010) (NUMBER X0010 TWO-OR-MORE)) /, X0817 
) ((((THE PROFESSOR /, XB008) (IN-THE-PRESENT (ADHIRE T X9888))) /, X 
(6809) ((((E TIME /, XW28) (IMXEDIATBLY-AFTER X0928 NIL)) /, X8021) ( 
AT-TIME (IN-TklE-PAST (BE-ING (ASSIGd X0809 NIL X007 7) ) ) X0i321) ) ) ) 
THE YHOPESSOR THAT I ADMIRE WAS ASSIGNING THE PROJECTS 
Example 2 
ONLY JOHN WILL LEAVE 1. 
SEMANTIC REPRESENTATION 
(( ((A INDIVIDUAL /, X0067) ((JOHN /, XB061) (NEQ X0@63 XM61)) ) /, X0 
862) (ASSERT I (NOT (IN-THE-FUTURE (LEAVE XB062))))) 
NON-NP PRESUPP&~ITIONS 
((JOHN /, X006l) (IN-THE-FUTURE (LEAVE X0061))) 
JOHN WILL LEAVE 
NP-RELATED PRESUPPOSITIONS 
((JOHN /, X006l) (*UNTENSED (IN-THE-SHARED-INFO X0061))) 
JOHN IWXST WUNTENSED- IN THE SHARED INFORMATION . 
ENTAZLMENTS 
Example 3 
T4AT DR SMITH FAILED TO CRALLENGE JOHN IS TRUE /m 
SEMANTIC Rl3PRESENTATION 
((JOHN /, X0045) ( (DRSSM1Ti-I /, X0044) (ASSERT I (IN-THE-PRESENT (TRUE 
(IN-THE-PAST (NOT (COME-ABOUT (EVENT (CHALLENGE X0044 X0045))))))))) 
NON-NP PRESUPPOSITIONS 
( (JOHN I, X0045) ( (DRSSMITH /, X8044) (IN-THE-PAST (ATTEMPT (EVENT (C 
HALLBNGB X0044 X0045)))))) 
DR SMITH ATTEMPTED TO CHALLENGE JOHN 
NP-RELATED PRESUPPOSXTIONS 
((DRSSMITti /, X08441 (*UNTENSED (IN-THE-SHARED-INFO X0844))) 
DR SMITH EXIST -UNTENSED- IN THE SHARED INFORMATTON 
((JOHN /, XBB45) (*UNTENSED (IN-THE-SHARED-INFO X0045))) 
JOHN EXIST -UNTENSED- IN THE SHARED INFORMATION . 
ENTAILMENTS 
( (JOHN /, X0045) ((DRSSMITH /, X0044) (IN-THE-PAST (NOT (COME-ABOUT ( 
EVENT (C~~ALLENGE ~0844 xaa4Sj)))) 1) 
DR SMITH FAILED TO CHALLENGE JOHN . 
((JOHN /, X084S) (.(DR$SMITH /, X0044) (NOT (fNwTHEuPAST (CHALLENGE X0 
044 X004S)) ))) 
IT IS NOT THE CASE TIiAT OR SMITH CHALLENGED JOHN 
Example 4 
THAT DR SMITH FAILED TO CHALLENGE JOHN IS FALSE /. 
SEMANTIC REPRESENTATION 
((JOHN /, X0048) ((DRSSMITH /, X0047) (ASSERT I (IN-THE-PRESENT (NOT 
(TRUE (IN-THE-PAST (NOT (COME-ABOUT (EVENT (CHALLENGE X004 7 X0048) ) ) ) 
)))I))) 
NOff-NP PRESUPPOSITIONS 
( (JOHN /, X0088) ( (DRSSMITH /, X0047) (IN-THE-PAST (ATTEMPT (EVENT (C 
BALLENGE X0047 X0048)))))) 
DR SMITH ATTEMPTED TO CHALLENGE JOHN . 
NP-RELATED PRESUPPOSITIONS 
((DRSSMITH /, X0047) (*ONTENSED (IN-THE-SHARED-INFO X0047))) 
DR SMITH EXIST -UNTENSED- IN THE SHARED INFORMATION . 
( (JOHN /, X0048) (*UNTENSED (IN-THE-SHARED-INFO X0048) ) ) 
JOHN EXIST -UNTENSED- IN THE SHARED INFORMATION . 
ENTAI LMENTS 
( (JOBN /, X0048) ( (DRSSNITH /, X0047) (NOT (IN-THE-PAST (NOT (COME-A5 
OUT (EVENT (CHALLENGE X0047 X0048) ) ) ) ) ) ) ) 
IT- IS NOT THE CASE THAT DR SMITH FAILED TO CHALLENGE JOHN . 
((JOHS /, X0048) ((DRSSMITH /, X0047) (IN-THE-PAST (CHALLENGE X0047 X 
0848) 1) 
PR SMITH CHALLENGED JOHN . 
Example 5 
DR SMITH SAYS THAT A STUDENT FAILED TO LEAVE /. 
SEMANTIC REPRESENTATION 
((E STUDENT /, X0052) ((DRSSMITH /, XO050) (ASSERT I (IN-?HZ-PRESENT 
(CLAIM X0050 (IN-THS-PAST (NOT (COHE-ABOUT (EVENT (LEAVE X0052) ) ) ) ) ) ) 
1)) 
NON-NP PREGUl?POSITIONS 
( {DRSSMITH /, X0050) (*UNTENSED (HUMAN X00SB) ) ) 
DR SMITH BE -UNTENSED- HUMAN 
((E STUDENT 1, X0052) ((DRSSMITH /, X0050) (IN-THE-PRESENT (CLAIM X(d0 
50 (IN-THE-PAST (ATTEMPT (EVENT (LEAVE X0052)) ) ) ) ) ) ) 
DR SMITH CLAIMS THAT SOHE STUDENT ATTEMPTED TO LEAVE . 
NP-RELATED PRESUPPOSITIWNS 
((DRSSMITH /, X0058) (*UNTENSED (IN-THE-SHARED-INFO X0050))) 
DR SMITH EXIST -UNTENSED- IN THE SHARED INFORMATION 
ENTAILMENTS 
( (E STUDENT /, XB052) ( (DRSSMI TH /, X0050) (IN-THE-PRESENT (CLAIM X90 
50 (NOT (IN-THE-PAST (LEAVE X0952))))))) 
DR SMITH CLAIMS THAT IT IS NOT THE CASE THAT SOME STUDENT LEFT 
Example 6 
-50- 
IE' JOHN LEFT /, THEN MARY APPRECIATED THAT HE LEFT /. 
SEMANTIC REPRESENTATION 
((MARY /, X0056) ((JOHN /, X0054) (ASSERT I (IF-THEN (IN-THE-PAST (LE 
AVE X0@54) ) (IN-THE-PAST (APPRECIATE X0056 (FACT (I N-THE-PAST- (LEAVE 
X0054))))))))) 
NON-NP PRESUPPOSITIONS 
( (JOfiN /, X0054) (IF-THEN (IN-TBE-PAST (LEAVE X0054) ) (IN-THE-PAST (L 
RAVE X0054) ) ) ) 
IF JOHN LEFT THEN JOHN LEFT 
((MARY /, X0056) ((JOHN /, X0054) (IF-THEN (IN-THE-PAST (LEAVE X0054) 
) ("UNTENSED (HUMAN X0056))))) 
IF JOHN LEFT THEN MARY BE -UNTBNSED- HUMAN . 
NP-RELATED PRESUPPOSITIONS 
( (JOHN /, X0054) (*UNTENSED (IN-THE-SHARED-INFO X0054) ) ) 
JOHN EXIST -UNTENSED- IN THE SHARED INFORMATION . 
((J0H.N /, X0054) (IF-THEN (IN-THE-PAST (EEAVE X0054)) ((MARY /, X0056 
) (*UNTENSED (IN-THE-SHARED-INFO X0056))))) 
IF JOHN LEFT THEN MARY EXIST -UNWNSED- IN THE SHARED 
I NFORMATION 
EWTAI LMENTS 
Example 7 
-51- 
IF JOHN MANAGED TO LEAVE THEN MARY WILL ADMIRE BIM / 
SEMANTIC REPRESENTATIQN 
( (MARY /, X0060) ((JOHN /, X0058) (ASSERT X (IF-THEN (IN-THE-PAST* KO 
ME-ABOUT (EVENT (LEAVE %00 58) ) ) ) (IN-THE-FUTURE (AQMIRE X0060 X0858) ) 
)I)) 
NON-NP PRESUPPOSITIONS 
(JOHN /, xaarP) (IN-TBE-PAST (ATTEMPT (EVENT (LEAVE ~0058) ) ) ) 
JOHN ATTEMPTED TO LEAVE . 
Nc-RELATED PRESUPPOSITIONS 
( (JOHN /, X0058) (*UNTENSED ( TN-THE-SHARED-INFO X0058) ) ) 
JOHN EXIST -UNTENSED- IN THE SHARED INFORMATIOW . 
( (JOHN /, X0058) (IF-THEN (IN-THE-PAST (COME-ABOUT (EVENT (LEAVE X00S 
8)))) ((MARY /, X00612)) (*UNTENSED (IN-THE-SHARED-INFO X0060))))) 
IF JOHN MAWED TO LEAVE THEN MARY EXIST -UNTENSED- IN THE 
SHARED INFORMATION . 
ENTAI LMENTS 
Example 8 
American Journal of Computations! Linguistics 
Microfiche 63 : 57 
IN'FORMATION CHANGES, 
CONCERNS, CHALLENGES: 
1977 N FAtS Annual Conference 
Chang~ng Role of Government 
Changer and Challenges in lndeAng 
Cohcerns rn Research 
Challenges of Deposited Documents 
Schedule of Events 
Tuesday, March 8, 1977 
8 00 a m -5 00 p m - Reg~strat~on 
(Roanoke, Rappahannock, and James Rooms) 
March 8-9, 1977 
9 00 a m 9 15 a.m Welcome and General Program 
In troduct~on 
Stouffer's Nat~onal Center Hotel 
Arl~ngton, Virginla 
N~neteenth Annual Conference 
Wecome. John E. Creps, Jr, 
NFAiS Pres~dent 
Engtneer~ng lhdex, /nc, 
Program Ou tl ine Russell /. Ro wlett, /r. 
7 9 77 Conference Program Chalrmon 
Chemrcal Abstracts Serv~ce 
9 15 a m -10 45 a,m Theme Session I The Changing 
Role of Government 
Information Programs 
Cka~rman Hubert E. Sauter 
Defense Supply Agency 
George P. Chandler, Jr. 
National Aeronautics and Space 
A dmmistrution 
Fred E. Croxton 
LI brarydf Congress 
William M. Thompson 
Defense Documentutfon Center 
NATJONAL FEUERATION OF ABSTRACTlNG & INDEXlNO S€RWES 
3401 MARKET STREET + PHILADELPHIA, PA. 19104 (215) 349-8495 
11:OO a.m..12; 15 p.m. Continuation nf Theme Session I 
A. G. Hoshovsky 
Department of Transportation 
Peter E Urbacb 
Notional Technical Information 
Service 
12:15 p.m.-2:00 p.m. Lunch Break 
(Attendees must make the~r own 
arrangements) 
2:00 p.m. 4:30 p m Theme Session I I: Indexing, the 
Key to Retrieval 
Cha~rman. Lois Granlck 
Amerrcan Psychoiog~cal 
Assoc~at~on 
Wednesday, March 9,1977 
8:30 a.m.-2:00 p.m. - Registration 
(Roanoke, Rappahannock, and James Rooms) 
9:00 a.m.=11.45 a.m. Theme Session Ill: Current 
Activities Related to 
Abstracting and Indexing of 
the National Science 
Foundation Division of 
Science Information 
Chalrmar, LeeG. Burchinal 
Nutlonal Science Foundutron 
* Techniques Used In Pr~nted 
Indexes 
A Keyword or Natural Language 
lndexlng 
Joyce Duncan Faik 
Amerrcan BI bliographical 
Center, CLIO Press 
B, Thesaurus or Con trolled 
Language lndex~ng 
Peter Clague 
INSPEC 
Ben-Am/ Lipetz 
Docb men tction A bstrac tr, 
lnc 
* Computer Generated lndexlng 
(re-indexing) for On-t~ne 
Retrieval 
Donlei U. Wilde 
New England Research Applrca- 
tron Center 
4.30 p.m.-5.30 p.m. 
NFAlS Assembly Business 
Meeting 
6:00 p,m.8:00 p.m. Conference-w~de Reception 
(Decatur Room) 
(speakers to be announced) 
- No coffee break th~s morning - 
11:45 a.m.-12:30 p.m. Miles Conrad Mernor~al 
Lecture 
Dr, William 0. Buker 
Bell Laboratorres 
*** 
Dr. Baker has long been actlve In sc~entlflc and 
technical Information matters at a natlonal level. He 
cha~red the panel of the President's Sc~ence Advisory 
Committee that authored the landmark study 
"lmprov~ng the Availabtllty of Sctentlflc and Technical 
lnformatlon In the Un~ted States" (The Baker Report) 
In 1958 He also served as chalrman of the Science 
Information Couscil of the Natlonal Science Founda- 
tton from 1959 through 1961 and was a member of 
the Welnberg Panel that produced the report 
"Sclence, Government, and Information" In 1963. 
He currently is a member of the Board of Regents of 
the Nat~onal Library of Medlclrle, a dlrcctor of Annual 
Revlews, Inc., a member of the Natbnal Commission 
on Ltbrrnes and Informatron Sclence, and a partici- 
pant In many her Important natlonal committees 
and cornmlsslons. 
The Mlles Conrad Memorlal Lecture was estabi~shed 
to honor G. Miles Conrad, first president of NFAlS 
This lecture IS "to be presented every year at the 
Annual Meeting of the Federation by an outstanding 
person on a sultable toplc In the field of abstract~ng 
and index~ng, but above the level of any individual 
serv~ce." 
12 30 p m 2'00 p m Conference Luncheon 
(Decatur and Farragu t Rooms) 
2 pin -4 30 p m Theme Session IV Deposrted 
Documents and Other 
Evolvlng Publicdticm Med~a 
Charrman lames L Wood 
Chemical Abstmcts Semce 
Karl k Heumann 
Federation of Amer~cnt~~ 
Societres for Experimental 
B~oloqy 
Latry X Besant 
The Ohio State ~nrve'rsit~ 
Albert L Bath 
Arner~can Sooety for Testmg 
and Materrols 
NFAXS 
3401 Market Str~et 
Philadelphia, Pennsylvania , 19104 
Telephone: (215) 349-8495 
N1 NFAlS Nmletter Subscr~ptron - -per calender 
yew hswed br monthly) Separate issues ava~lable 
htSsmerCh 
REPORT SERIES 
R9 Pooirrm Statsmt on SATCOM Hooort, January, 
1970, Ssm 
R6 Nltlod Watron of Abstrretinp and lndexlng 
%nr~t%c Member Ssnr~ce Descriptions, July, 1973 
s6,w 
FPl1 On L~ne Commands Chart (A Quick Users Gu~de 
for B~bllographic Search Systems) Barbara Lawrence 
md Barbara G Prew~tt May, ID75 
$1 00 
FP12 KEY PAPERS (On the Use of Computer-Based 
Brbtiographlc Servtces) Joint publl~atron w~dr 
Ameriqn Soc~ety for Informa'tron Scrence 
October, 1973 $1000 lASlS 81 NFAlS Mem 
ban SO01 
NOTEm Contans Federstlon Report No 2 Data 
Element Defind~ons for Secondary Ssnrles Jme, 
197 1, and Report No 4, The Canallan National 
Sclent~irc and Technical Informatian (STI) System, 
A Progress Reporb Jack E. Brawn (1972 Mlles 
Conrad Memorial Lecture, May, 1972) 
CP2 1070 Comerence Digest, aorton, Me# , September, 
1970 $7 50 
RO ~eirl#llj.teratum Idsatan. Prqst wppor~d CP3 1988 Annual Confe~.ence Proceedrngs, Rqlergh. 
by M$C &IS Canttact C8?3 May, 11975 $6 00 
hl G, September, 1970 $10 00 
I 
CP4 1983 Annwl MstriiSAIS, Wd~ngton, 
D C . Much #)22,1- (Contains tlw National 
Amencan Journal of Computational Linguistics Microfiche 63 : 60 
for the Advancement of Scie~e 
BECKER AND HAYES, INC. 
- 
1 1661 SAN VICENTE BLVD. 
SECTION QN INFORMATION AND COMMU NICATION-T LOS ANGELES, CALIFORNIA 90049 
JOSEPH BECKER, Secretary (213) 820-2683 
Eugene Garfield 
fnstitute for Scientific Information 
MEMBER-AT-LARGE Richard H. Belknap 
(SECTION COMMITTEE) National Research Council 
NOMINATING COMMITTEE Marilyn C. Bracken 
Chevy Chase, Maryland 
John W. Murdock 
Informatics, Inc. 
~{EW FELLOWS OF SECTION T 
Lee G, Burchinal 
Ruth M, Davis 
Odom Fanning 
EMERGING NATIONAL AND INTERNATIONAL POLICY ON INFORMATION Frame 61 
BEYOND GUTENBERG: COMMUNICATION WITHOUT PAPER? Frame 63 
A CYBERNETIC APPROACH TO ASSESSMENT OF CHILDREN Frame 64 
INTERNATIONAL COMMUNICATION IN BIOMEDICAL RESEARCH Frame 66 
THE MANY FACES OF INFORMATION SCIENCE Frame 67 
The following frames contain lists of participants and summaries of 
symposia as furnished by organizers in the fall of 1976. 
Nembers, Section T 
23 Bovernber 1976 
Page 3. 
EMERGING NATXOfiAJ; AND ZNTERNATIONAI; POLICY 
Arranged by L. B. Heilprin (University of Maryland): with 
E. B. Adams (George Washington University): A. A. Aines 
(National Science Foundation): and, G. Chacko (University 
of S~uthern CaliZoPhia) 
Tuesday ,' 22 February Holiday Inn, Silver Plume 
9:00 a.m. Presiding: ~lizabeth B. Xdijrns (Assac. Prof., Management, 
George Washington university) 
Impact of Science and Technology on Information Systems 
Joseph C. R. Licklider (Prof. of Elqcerical Eng., MIT) 
Impact of New Technology on National Copyright Policy 
Arthur J. Levine (Natl. Camm. on New Technol. Uses 
of Copyrighted Works) 
FCC Policy Towards Computers and Communication 
Donald A. Dunn (Prof. of Eng . Econ, Systems, 
Stanford University) 
Are there "Responsible Computer Systems" and is a 
National Policy in Sight? 
Ruth M. Davis (Dir., Inst, for CoMputer Sciences 
and Technol., Natl. Bur. Standards) 
Discussants: Elizabeth B. Adams, Ruth M. ~avis, 
qonald A. Dunn, Arthur J. Levine, and Joseph 
C. R. Licklider 
3:00 p.m. Presiding: Laurence B. Heilprin (Prof. Emer. of 
Info. Science, University of Maryland) 
Conflict and Agreement Between National and International 
Policy on Copyright 
Barbara A. Ringer (Register of Copyright, Library 
of Congress) 
Global Probems in Internationa1,Information Sharing 
Andrew A. Aines (NSF and Natl. Comm. on Lib. and 
Info. Science) 
Is an Interntional Policy or Meta-Policy on Information 
in Sight? 
~onald G. Fink (Exec. Consultant, IEEE) 
Discussants: Andrew A. Aines, Lewis M. Branscamb, 
Donald G. Fink, Laurence B. Heilprin, and 
Barbara A. Ringer 
Members, Section T 
23 November 197 6 
Page 4. 
Evidence that the post industrial society is an information 
society includes proliferation of data and informatio-ng 
community of researchers and writers, processors and disseminators ; 
increase in number and variety of channels that handle and deliver 
information; institutionalization and internationalization of 
informa,tion systems, networks, programs. A few countries, with the 
United States in the van, are emerging as information societies 
employing electronics as key means of information banking and delivery. 
As we enter this age economists, political scientists, s6ciologists, 
legislators and public administrators deal increasingly with issues 
which may emerge as a composite natidnal policy on information. In 
parallel, international issues and policies are taking shape. The 
morning sesslon will consider national policy,. 
The afternoon session will extend the discussion to international 
policy. Special emphasis will be placed on needs of developed and 
developing countries and on information barriers that separate them, 
including the imperative to remove barriers as wisely as possible. 
Each session will end with a "blue sky" discussion, open to the 
floor.. They will consider such matters as obstacles to flow of 
knowledge in existing national and international channels; possible 
impacts of yet new telecomunication technoIogy; need fqr policies 
concerning conduits of knowledge and the flow of scientific and 
technical information; and the need to facilitate the one-world 
global village thrust deriving from the information technology and 
Other revolutibns. 
(Sponsored by AAAS Section T. cosponsored by Section P and by 
the American Society for Information Science) 
Members, Section T 
23 November 1976 
Page 5. 
BEYOND GWENBERG,: JJDMMUNICATZON WITHOUT- PAPER? 
Arranged by Harold E. Bamford, 3r. (Program Di~ector, Access Improvement 
Program, National Science Foundation, Washington, D.C.) 
Wednesday, 23 February 
Holiday Inn, Cripple Creek 
9:00 a.m. Presiding: Harold E. Barnford, Jr. 
An On-Line Intellectual Community 
Dr. Murray ~uroff 
Getting and Using Scientific Zafokmation at a Computer 
Terminal 
Dr. Williafi Paisley 
Access to Computer-Readable Data and Literature 
Dr. Roger Summit 
Recording Newly Discovered Information 
Mr. Davld Staiger 
Toward an integrated Communication System 
Dr. George Chacko 
Even if the paper-based communication system of science 
can 
continue to expand with the body of knoQledge and the population of 
usexs, it offers little hope that scientific information will ever be 
much more readily accessible than it is today. An attractive alternative 
may result from the marriage of computer technology with telecommunications 
The panel will discuss various options of this electronic alternative, 
considering their likely impact an user productivity and demand for 
information services, their technical and economic feasibility, their 
legal and policy implications, and obstacles to their realization. In 
preparing their presentations the panelists will have engaged each other 
ov'er a period of months in computer conference, one of the techniques 
whlch they will discuss. 
ISponsored by AAAS Section T) 
MeIllbers, Section T 
23 Nov~~S 1976 
Page 6. 
TOWARD THE HUMAN USE OF HUMAN BE~Gs : 
A CYBERNETIC APPROACH TO ASSESSMENT OF CHILDREN 
Arranged by Mark N. Ozer (ASSOC. Prof. child Health & Development, 
George Washington School of Medicine, Washingtonf D.C.) 
Wednesday, 23 February Holiday Inn, Silver Heels 
3400 p.m. presiding: Frank Baker (Dix, ~iv. Comrnun, Psych., 
SU~Y, Buffalo, NOYO) 
The Joint Regulation of Infqnt-child Interaction 
T. Berry Brazelton (Assoc. Prof. Ped., Harvaxd 
Med. Sch., Boston, Mass.) 
A Cybernetic Approach to Psy~hological Testing 
Irving E. Sigel (Educational Testing Service, 
Princeton, N. J. ) 
Cybernetic Testing 
Bernard Brown (Div. Res. & Eval., Off. child ~evel., 
Washington, D,C,) 
Assessment as an Interactive Process 
Mark N. Ozer 
  is cuss ants: William Powers (author, Northbfook, Ill,) 
Historically, cybernetics has tended to focus on the interaction 
between people and machinks. Cybernetic issues of control and feedback 
of information are to be explored in this symposium as they relate to 
human interaction. The application of these ,issues to human systems 
requires an awareness of the sharin of control and informational 
+ 
feedback as the aspect to be hlghlig ted. More specifically, the 
assessment of children will be explored as a place to illustrate the 
value or this concept. 
The traditional testing process has viewed 
the subject as someone who is to be manipulated by the examiner, The 
application of a cybernetic approach to assessment offers a model for 
the revision of the power relationship that has direct relevance to 
the process of child development. The examiner is intent upon the 
effects of the very process ~i examination on the person being assf?ssed. 
In order to sample the process of child development, the examiner must 
now stimulate it. The individual being examined must become aware of 
some reciprocal effect upon the examiner as a simulation of what 
happen9 in the natural process of growth and development. Assessment 
is viewed as tnore nearly an interactive process between the individuals 
involved. The person being examined is no longer merely subject to the 
AAAS Section T 65 
examiner. With even rather young children, it becomes pssible to 
make suoh reciprocal effects explicit by providing feedback as to the 
value Of the input pr~vided to the inteyaction. 
It is the feedback 
as to the reciprocity of the relationship that is the cr~cial parameter 
that distinguishes the human use of cybernetic concepts. 
(Sponsored by the American Society for cybernetics and AAAS 
Sections 3, T, and Q) 
SCFENCE INFORMATION 
XNTERNAT IONAL COWUNICATION FOR 
RESEARCR XN BIQMEDICINE 
Arranged by Arthur W. Elias (Difector of Professional Services, 
BioScience Informatioh Service, Phila., Pa.) 
Wednesday, 23 February Denver Miltgn, Beverly 
3:00 p.m. Presiding: Arthur W, Elias 
~ommunications for Research in Biomedicine in the 
united Kingdom and Commonwealth Countries 
~rian Perry (British Library) 
Communications for Research in Biomedicine in 
Western EU~QP~ 
Rolf Fritz (dimdi) 
Communications for Research in Biomedicine in Canada 
George Ember; (National Research council) 
~ommunications for Research in ~iornedicinti? in 
Scandinavia 
Goran Falkenberg (MIC, Karolinska Institutet) 
Communicatiohs lor Research in Biomedicine in the 
United States 
Mary Corning Pational Library of Medicine) 
Communications for Research in Bibmedicine in UNISIST- 
The World System 
Lee Burchinal (National Science Foundation) 
The symposium will attempt to bring together authoritative 
decision makers in the fields of biomedical information retrieval 
from the scientific world. 
It will try to redate national activities 
of the present in supporting biomedical research through lnfarmation 
activities and to forecast future impacts and developments. 
In 
addition to national plans, the symposium will attempt global 
perspectives in relation to regioha cooperation (eg. EEC) and 
overall programs ('JNISIST) . 
(Sponsared by AAaS Section T) 
THE MY FACES OF INFORMATION SCLENCE 
Arranged by' Edaard C. Weiss (Program Director, Information Science 
Program, Division of Science Information, National Science 
~aundation, Washinqton, D.C.) 
Friday, 25 February Denver Hi1 ton, Denver 
9:00 a.m. Presiding: Edward C. Weiss 
An Integrated Theory of Informatidn TranSfer 
William Goffman (Dean, Sch, of Lib. Science, Case 
Western Reserve University) 
Theoretics 7f Information for ~ecision-Making 
Marshall C, Yovits (Chrn., Dept. of Computer and Info, 
Science, Ohio State University) 
Information Structures in the Language of Science 
Naomi Sager (~inguistic String Project, N.Y. University) - 
Knowledge Transfer Sys terns, 
Donald J. Hillman (~ir., Center for Info. Science, 
Lehigti ~niversi ty) 
The Portent of Signs and Symbols 
vladimir Slamecka (Dir, School of Info. and Computer 
Science, Georgia Institute of Technology) 
THis symposium will examine the various faces of information 
science as an emerging discipline. The growth in the development 
of digital technology in,the last quarter century has been phenomenal, 
yet there is 'a surprising mismatch between the high capacity of the 
technology and t;he lagical level at which it is employed for 
infohation and retrieval. The problem appears to be with the state 
oP we diskipline itself; we have been trying to develop and apply a 
technology without having a well-developed scientific foundation upon 
which to support it. A discipline rests on three major parts: a 
science, applicatians , and education; -each part 'must support the 
others, In information sclence, the weakest, component today is the 
science itself. Two questions emerge: what does information science 
consist 04 and how can we skrgnqt.hen it to provide sou'hd theoretical 
structure from ~hifh~future'applications will derive. The pu'rpose 
of this symposium is' to review the current status and explure 
posbibilities for break-throughs. 
(Spansofed by the American Society for Information Science and 
the AAAS Section T) 
American Journal of Comp~tati~nd Lhgd~tia Microfiche 63 : 68 
New Journai 
EDITORS 
I 
An Interdlsciplina~ Tluarterly crf bnguage Studlea 
1 Aims and Scope 
e~ghboring disciplines 
ng interest in human linguistic 
f langhage systems). 
n the sb'cial aspects of 
language acqu&mon, 
ge In context", theory 
heory of soC~al actlon, 
of the growing ins~ght 
1st~ activltyl is social 
as been men1fe3t for 
heoret~cal discipline, has formu- 
e area of the theoretical foundations, 
r understanding and 
e of language as ona of mhn's tools for 
'soc~etal' ~nteraction. 
first journal to aim 8t 
pragmat~c stud~es of 
, and will cover all aspects involved It will attempt to 
alds of soc~ohnguisr~cs, 
psychohnguistics, man-machine interaction, applied linguistics, 
I and several other areas. 
The advlsory editors will nor only act as speclali&s in their 
respective fMds, but wilt furthermore attempt to integrate 
developments'or~ginatng in diffarent tsclentlfic as wett as geo- 
~raphlcall areas, thereby providing a forum for mutual infor- 
mation and Increased debate on ongoing research and practical 
projects. Linguisr~, anthropologists, philosophers of Istnguage, 
as well es workers from related fieldswll find much of interest 
JACOB L, MEY 
Odense Univera~ty 
Ni& Bohrs All& 25 
OK-5000 Odense 
Denmark 
1 HARTMUT HABERLAND 
Roskllde U nlverslty Center 
P 0 Box 260 
DKJlOOP Roskllde 
Denmark 
REVIEW EDITOR 
FERENC KlHER 
Hu nqanan Academy of Sclences 
SzenthiSromdg utca 3 
ti-1012 Budapest 
Hungary 
Board of Advisory Editors 
in the artcles now belng prepared for the forthcoming issues 
by experts in the varioushareas of linguistic pragmatics. 
J. Allwood, Unlversrty of Gothenbutg, 
Sweden 
P.B. Anderaen, Unnrerslty of Aarhus, 
Denmark 
T Andenen, Aalborg Univwsrty Center, 
Denmark 
R. Bartsch, Un~vdrs~ty of Amsterdam, 
The Netherlands 
R.M. Blakar, Unmrsity of Oslo, 
Nomy 
S. DTk, Unnrerslty of Amsterdam, 
The Netherlends 
N. Dittmar. Universtty of Heldelberg, 
W. Germany 
G. Drschmah, Unhrersrty of Salzburg, 
Austria 
na, 1 , 
Editorial: Pragmatics and lingui'stics IH. Haberland and Meyl, 
Assertions, conditional speech acts, and practical inferences ID. 
Wunderlich). School prowms of regional dialect speakers: ideology 
and reality. Results andmethods of empiricak investigatibns in South- 
ern Germany (V. AmmonJ. Methbddogical questions about artificial 
intelligence : approaches to understanding natural language (Y. Wilksl. 
The classification of question~answer structures in English (M. 
Baumert), Reviews. 
iio. 2 
What is a theory of use? (A. Kasher). Patterns in purported speech 
acts (0. Hackman). "I'm dead". A lingy istic an;lysis. of paradoxical 
techniques in psychotherapy (S. Tlo"rnei-~oetr and D. Franck). 
Some analogies between adaptive search strategies am$ psychological 
behaviour IG. Engstrom). Language acquisition as the acquisition. of 
speech act competence IH. Ramge). Reviews. 
no. 3 
Pragmatique et rhhtorique discursive (W. Settekorn). How, to under- 
stsnd misunderstanding : 'Towards a linguistic explanation of under. 
standing ID. Zeefferer). Towards a theory of pragmatics IH. 6iml. 
The concept of function in recent Soviet linguistics (E Pssierbskyi. 
Reviews. 
no. 4 
On soalled "rhetorical" questions IJ. Schmidt-RadefeItI. On the 
concept of communicative competence: some consequences for 
the teaching of language (K. Sornigl Some refnarks-on "explanation" 
in recent sociolir)guistic work IN. Dittmar). The formation d role 
K. mpt. Unbrski of Malburg, 
W. Germany 
V. Ehrich, Univedty of DiicweMdn, 
Gemny 
P. Elsenberg, Twhnicsl University, 
Hannover, W. Gmany 
C. Rllmorb, University of California, 
Berkeley, U S.A. 
D. Franck, Unlversjty of Niimegen, 
The Netheriands 
TL GlvCn, University of California, 
Los qngeles, U S A. 
K. Gloy, Un~versh fyf Du~sbu~, 
W Germany 
N. Goldman, University of Southern 
Cakfornia, U S.A. 
F Grsgersen, Untversity of copenhagen, 
Denmark 
D.G. Hsyr, State Unnrersrty of New York 
at Buffalo, U S.A. 
M A K. Halliday, Universw of Sydney, 
Australla 
R. Hesen. Macquarie University, 
North Ryde, N,S W., Australla 
G. Hubers. Un1venit-y of Amsterdam, 
The Netherlands 
D. H ymes, Unlverslty of Perimylvania, 
Philadelph~a, U.S A 
A. Kasher, Bar llan University, Tel Aviv, 
Ikrael 
G. la koff. Unlverstty bf Califoma, 
Berkeley, U S A , 
A. Malikoutj-Drachman. Un~vrffs~ty of 
Salrburg, Austria 
C Montgomery, Operat~ng Systems, 
Inc ,Woodland Hills, Cahfornia, U.S.A 
U Quasthoff, Free University, 
W. Berl~n 
R. Sohank, Yale Unlvetslty, New Haven, 
Connect~cut, U S A 
K Sornig, Unnrenlty of Graz, 
Austrie 
T. Suzuki, Keio Univers~ty, Tokyo, 
Japan 
M.3. White. Univers~ty of Ghent, 
Belgrum 
Y Wilks, Un~verslty of Read~ng, 
North-Holland Publishing Company 
P.O. Box 21 1 - AmStetdam - The Netherlands 
- 
concepts in texts: The concept "Mother" in German schoolbooks 
(1. Kumrnerj. an the distinction between presuppositions and conler- 
sational implications ITh. Kotschil. Reviews, 
England 
D. Wu nded ich, n,VBmw of D..s981 dad, 
W. Germany 
American Journal of Computational Linguistics 
Micmfi~he 63 : 70 
AUTOMATIQUE 
INFORMATlQlJE 
MATHEMATIOUES APPLIQUCES 
RECHERCHE OPERATIONNELLE 
DIVISION ~~IEORIE ET TECHNIQUE DE L' INFORNATIQUB 
PRESENTATION DES ACTXVITES DU GROUPB DE TRAVAIL 
"Analyse et Expgrimentatian dans les Sciences de 1'Houme 
8 par les MGthodes informatiques" 
t. 
r, 
I NFORMAT IQUE I EITERACTIVE ET SC I ENCES DE L' HOME 
F 
SYSTEMES-ET LANGAGES JNTERACTIFS 
COMME ELEMENTS CONCEPTUELS DANS 
L' ELABORATION D ' UNE D~ARCHE EXPER INENTALE, 
EN SCIENCES HUMAINES 
Le d6velappement rapide des mgthodes et techhiques interactives et 
l'utilisation croissante de systemes et/ou de langages interactifs dans des d6- 
marches exp6rimentales dans les sciences humnines ont conduit le groupe de travail 
de 1'A.P.C.E.T. "Analyse et exp8rime1nfation dans les sciences de l'homme per ies 
m6thodes informatiques"ii otganiser ses nctivitGs, pour cette ann$e, autour du 
thEme gkngral "Infonnatique interactive et qciences de l'honme". L'ktude appro- 
fondie de certains aspects de ce thgme contribuera ii Eclairer un ensemblz de 
questions li6es 5 l'introduction de ces h16thodes dans les disciplines des sciences 
de l'homme. Cette riSflexion permettra, sans nu1 doute, de faire @merger des axes 
de recherche dont les objectifs correspondent 2 ceux que le groupe s'est fix6 
lors de sa crfation, il y a maintenant plus dvun an. 
Des travaux technologiques importants ont abouti 3 la conception d'or- 
ganes d'entr6e-sortie trSs sophistiqugs - tglgtypes, affichage visuel alphanume- 
rique, graphique, claviers spGciaux, photosryles, etc, - appropri6s au dialogue 
home-machine. Parallalement 2 leur rdalisatibn, de nombreux logiciels interactifs- 
systzmes, langages, procgdures orientGes, etc. - ont tit6 dGveloppds, implkment6s, 
et reldus op6rationnels. 
L'expgrience montre que de tels dispositifs - ordinateurs, 
interfaces, logiciels - ant EtG utilis6s pour contribuer 5 rgsoudre une large 
variEt& de problBmes dans on nombre trgs divers de disciplines. Des 6tudes sur les 
diffgrents modes dlinteraction impliqugs par ces travaux ont port6 essentiellernent 
sur les aspects techniques des liaisons, sur ceux des systsmes et de la communi- 
cation et enfin stlr le comportemc~t psychologiqutr des utilisateurs. Nearnoins 
les modalitgs d'insertion de telles machines, tant du point 
de vue m~thodologique 
que du point de vue technique, dans des dispositifs exp6rimentaux nlone que rare- 
ment fafc l'objet de recherche sp6cifique et approfandie. L'unc des raisons essen- 
tielles de cette lacune rt5si.de dans le fait que ce type de reflexion se situe a 
la frontiere des mEthodes de l'informatique interacti;e et de celle du darnaine qui 
les utilise. 
La complexit6 de la structure des donnges et des traitements B operer - 
analyse et statut des donnEes par rapport 3 certains objectifs, formulation d'hy- 
pothsses, dgterminatton de mod2les, Gvaluations et validations des resultats, etc. 
- dans le domaine des sciences de llhomme pose de manikre plus aigue le problsme 
de l'insertion et de l'utilisation des m6thodes et techniques interactives dans 
la conduite dtexpiSriences. L'examen des questions liges a cette introduction 
devraitconduire 2 dggager des th2mes de riiflexion sur la contribution mEthodolo- 
gique de ces 6lGments dans la conception et ltblaboration de toute experience, 
ainsi que sur les modifications 6ventuelles que ces methodes peuvent apporter 
dans le dgroulement du pr'acessus experimental. 
L'inventaire xaisonng des possibilitBs conceptuelles offerts par les methodes 
interactives et leur intggration logique dans tout dispositif expihimental fon- 
dent le programme des activitgs du groupe de travail qui sera en consi?iquences 
centre sur le sujet suivant ! Sys~Srnes e? $angages interactifs come 614pents 
conceptuels dans 1 '&laboration dt une dGma,rche expkrithentalC dans les .sciences 
- 
de 1 'home". 
PROGRAMME DES SESSIONS 
Ce programme ss dQcornpose en 4 sessions. 
I/ La premisxe sera consacr6e B lt6tude comparative de deux dispositifs 
interactifs en relation avec la conception architecturale (Vendredi 11 F6vrie1-1977). 
2/ La probl6matique de l'insertion des mgthodes interactives dans un 
dispositif expsrimental en sciences humaines fera l'objet de la deuxiGme session, 
qui durera 2 journQes, les, 17 et I8 Mars 1977. 
3/ La troisihe portera sur l'dtude du d6veloppement des mgthodes inter- 
actives en sciences humaines (juin 19775. 
4/ Enfin la quatribe session fera la synthsse. cle ces travaux dans le 
cadre d'un atelier organisg parallslement au dtiroulement du congrgs de ~'A.F.C.E.T. 
"~od6lisation et ~aitrise des ~ystZmes" qui se tiendra ii Versailles les 22-23-24 
Novembre 1 977, 
Les Animateurs : E. CHOURAQUI 
J. VIRBEL 
xx Pour tout renseignement ou prise de contact concernant le groupe de travail 
et le programme de l'annge 1977, 
stadresse,r B : E. CHOURAQUI ou J. VIRBEL 
C,N*R.S,-L*T*S*H* 
31, Chemin Joseph Aiguier 
13274 MARSEILLE C6dex 2 
Tg1, (91) 75.90.42, 
AUTQMATKlUE 
INFORMATIQUE 
MATHEMATIQ~)ES APPLIQUEES 
RECHERCHE QP~RATIONNELLE 
QIVISION TTI 
"Analyse et Expsrimentatian dane' les Sciences de l'l'lome 
par les Mgthodes iaf ormatiques" 
Animateurs : ED CHOURAQU'I, J. VIRBEL 
C.NmR.S.-LmImSeB* 
31, Chemin Josspfr AiguQer 
13274 MARSEILLE C6dex 2 
T;EME DES ACTIVITES : "Inf oxmatique interactive et sciences de 1 'home" 
Sys tSmes e t langages interactif s comma &f&nents con- 
ceptuels dans 1' Blaboration d'une d6marche expBrimen- 
tale dana les sciences de llhome. 
SESSION 1 
DATE : Vendredi 11 FBvrier 1977 3 10 Hedres (toute la jouraecr) 
- 
LIEU ; Ecole d1Atchitectwe de Marseille~Luminy 
- 
Salle de Confgrence du GAMSAU 
TITRE : Pri5sentation et Comparaison des objectifs et des hypothsaes d'utilisation 
- 
dlARLANG et de TROPIC 
INTERVENAN_TS : M, LATOMBE, ENSEGP (Grenoble) 
MK. AUTRAN, FREGIER, RODRTWEZ,, ZQLLER, GWAU (Mar sei 1 le-Luminy ) 
msum t 
- 
L'idOe essentielle du Systsme TROPIC est de pernettre au concepteur 
de d6crire un probl&me en termes principalement d6claratifs pour obtenir une 
solution produite automatiquement par le systsme. Celui-ci est suffisamment 
ggn6ral pour pennettre de travailler dans des disciplines diff6rentes. 
11 mat 
en oeuvre des techniques d'htelligenca Artificielle dont les Blhents les plur 
interessants sonr : la reprgsentation des connaissances entitem, un rn6canfsme 
de si5lection des connaissances utiles, l'application d'une strategic descendants, 
la collaboration de deux programmes de r6solution de probli3mes, une technique 
de retour (backtrack) iSvolu6e et une proc6dure d'apprentissage. 
Le but du langage ARL~JG est de fournir aux concepteurs de l'amgnage- 
ment un odtil de description des donn6es et de recherche de solutions B leurs 
problsmes par des proc6dures interactives compatibles avec leur prafique ou 
entra?n&nt des modifications acceptables de leur dharche. 
Le concepteur d6cri.t les donnses sous fsrize dTarborescence de descripr 
tion munie d'opgrateurs "et" et "ou". 
La recherche de solutions sqeffectue pax 1'6critul"e de blocs de pro- 
grammes permettant' : 
- de tgaliser des algorithmes de traitement des donnges decrites 
- de spgcifier la ssmantique opkratoire de relations descriptives 
- d'obtenir des donnses dynarniques dgcrites potentiellement et ggngrges 
par algorithmes . 
Ces diffgrentes actions autorisent la crktian et l'enrichissement 
d'une base donnges, 3 chaque modification correspond alors un 6tat de la base 
qui peut Stre conserd s'il est jugs pertinent par le concepteur.  ensemble 
des Etata conserv6s constitue la trace du processus de conception. 
American Journal of Computationd Linguistics 
Microfiche 63 .- 75 
From The Linguistic Reporter, A newsletter in applied 
linguistics, Pubdished by the Center for Applied 
LfnguPstics, 1611 North Kent Street, Arlington, 
Virginia 22209. Volume 19, Number 4, January 1977, 3. 
Stanford Phondogy Archive 
InvYtes Retrieval Requests 
The Stanford Phonology Archive is an NSF-sponsored In stop systems with a voicing contrast, which 
rofect whose goal is to compile a corn uter-accessi- segments are more frequently missing from a cam- 
[la file of phonelic and phonologicaf lnf orma tion plete p hone pic paradigm? 
based on an areally and gentically balanced sample What is the most common environment for the 
of 200 languages [including the 11 most widely spoken voicing of voiceless obstruants; for the spirantiza- 
languages in the world). Operationally, the Archive tion of stops, for shifts in point of articulation; for 
staff 
encodes, and computerizes informa tion vowel backing or fronting, for nasalization? 
found in ~ub'is'ed ~honelic ""0' ~ho'ological 
sewices are currently performed free 
descriptions* so 
data "om different languages can of 
upon request. The Archive staff, however, 
be accurately and meaningfull~ com~ared. The ~roj- places the following limltalions bn its capacitiks: 
ect, which began in 1971, is currently in its final corn- since they are 
in the process of refining and aval- 
pilation and formalization stages, 
uating material, some of the information in the Ar- 
One of the Archive's major functions is to pmvide is Still in 121 Archive 
a information 
service lo members Of no syntactic,elexical, or textual data for any language; 
linguisttc communit~. Some of the 
which (3) the ArC:..re is synchronic; (4) although 
compose the Archive's data base. include: specific ,,st correspondence2 ls qnswered as it is received, 
phonetic segments and/or ~~~~~~~g~~~~ Processes 
there may be occasional delays in processing, 
'lasses of segments Or P~~~~~~~~)~ Ihe Or 
The specific fields of data available for searching 
areal distribution of segments or processes; systems 
of phonemic contrasts for classes of segments [such as me described in greater detail in a publication en- 
tones, nasal consonants, oral vowels), patterns of seg- titled A Reference Mc~nual and user's Guide for the 
ment alternations (allophonic or morphophonemic), Stanford Phonology Archive** Copies are available 
the effects of specific segments in proximate 
for $5 00 from Dept of Ling. ~tanford U. ~tanford 
tioning environments; phonotactic constraints in vari- CA 94305 
ous word and syllable positions; descriptions of stress- 
accent systma or syllable structure. 
Extensive use has been made of the Archive's data 
base, and some sample requests submitted and an- 
swered include: 
*Are assimilat~on rules primarily preservatory 
(progressive) or anticipatory (regressive)? 
What are the phonotactic constraints on word 
and syllable-ini tial consonants? 
*How is the distribution of front rounded vowels 
limited areally? 
Does every Ian uage which has rising tones also 
have at least one fa1 f ing tone? 
Do nasalized vowels tend to be more mid in 
height than corresponding oral vowels (i.e., lowered 
if high, raised if non-high)? 

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Chard&, Eugene, ~lT~ard a Model of Children's Story ~ensionv. 
Artificial Intelligence Laboratory Report AI TR-266. 
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Charniak, Eugene, "Jack and Janet in Search of a Theory of Knowledgen. In 
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Clark, H. M. and Haviland, S . E. , ~Corrrprehension and the Given-New Con-tpaetIt, 
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F5.lImre, C .Ye , "Verbs of Judging: 
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Holt , Rinehart , and Winston ,ml. 

Givon, Tdlmy , "The Time--Axis Phenomenontt. 
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890-925. 

Harris, Z . S . , "Tho Systems of Gr- : 
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Haviland , S . E . and Clark, H.M. , "What ' s New? Acquiring new information 
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Isard , S . , "What Would You Have Cone If. . . ?" Unpublished, 1974. 

Joshi . A, K. . and Weischedel, R.M. , ''Some Frills for PMal Tic-tac-toe : 
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kttunen, Lauri, "On the Saxintics of Complement Sentences". 
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Karttunen, Lami, llPresupposit ions of Compound Sentences". 
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KArttunen, Lauri, "Presupposition and Linguistic Context". Tneoretid 
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Keenan, Edward, L. , A Logical Base for Ehglish. Unpublished Doctorel 
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KeeMn, Ehyard L., Kinds of Presupposition in Mtml Iimguagesff. In 
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Keenan, Edward L., "On Semantically Based (2immtT. 
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K,,iparsky, Paul ad Carol, "Factfl. h Steihbwg and J&bovits, 
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Wff, George, HPresupposition and Relative Well-fomednesstl. 
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Lehnert, Wendy, "What Makes Sam Run? Script Based Techniques for Question 
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Sager, Naomi, 'The String Parser for Scientific Literature". In Rustin, 
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Schank, Roger C . , and Reiger , Charles J. , 111. "Inference and the Computer 
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Schank, Roger C., "Using Knowledge to Understand". In Proceedings of the 
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Smaby , Rid-md, "Consequence, Presuppositions and Cmeferacetl. To appear in 
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Weiscbedel, Ralph M. , ltCaputatifn of an Unique Class of Inferences : 
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Weischedel., Ralph M. "A New Semantic Computation While Parsing: 
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and Ehtaimt ,Iv Technical Report 676, Dement of Mmtion and 
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Wilks, Yorick, "A Preferatid, Pattern-seeking, Semantics fcr Natural 
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Winogmd, T. , Understanding Natural Language. 
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Woods? W.A., "TMnsition Network Q?am~xs far Natwal Lan&uage AndZy~is~~, 
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Woods, U.A., "An Ekperhtal Pamhg System far TMnsition Network GMmnarsrt. 
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